Section_3_Sklearn.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/rrajasek95/nlp-243-notebooks/blob/main/Section_3_Sklearn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XFnMHzL8Z7CO"
      },
      "source": [
        "# NLP 243 Section 03 - Classification\n",
        "\n",
        "In this section, we will be using SciKit-learn and a few other libraries to approach the problem of text classification. We will go through the entire pipeline of reading data, preprocessing, feature creation and ultimately, prediction. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xCBwoy_Z9oh"
      },
      "source": [
        "## Dataset\n",
        "\n",
        "We will be using the **Large Movie Review Dataset** for this particular task. This comprises of highly polarized movie reviews labeled as either positive or negative sentiment. There are 25k training examples and 25k test examples.\n",
        "\n",
        "Link to the dataset page: <a>https://ai.stanford.edu/~amaas/data/sentiment/<a/>"
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "G8R1diEIX2fn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "562d7196-baf1-496e-ddc2-ca81d42075e5"
      },
      "source": [
        "!wget https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n",
        "!tar xvf aclImdb_v1.tar.gz"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "aclImdb/train/unsup/44982_0.txt\n",
            "aclImdb/train/unsup/44981_0.txt\n",
            "aclImdb/train/unsup/44980_0.txt\n",
            "aclImdb/train/unsup/44979_0.txt\n",
            "aclImdb/train/unsup/44978_0.txt\n",
            "aclImdb/train/unsup/44977_0.txt\n",
            "aclImdb/train/unsup/44976_0.txt\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rG0fpioHbUfq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "72c74445-13dc-4e36-e1a6-c4fed54a3cc3"
      },
      "source": [
        "# Data Examples\n",
        "!echo \"Positive Example:\"\n",
        "!fold -w 80 -s aclImdb/train/pos/1_7.txt\n",
        "!echo ''\n",
        "!echo ''\n",
        "!echo \"Negative Example:\"\n",
        "!fold -w 80 -s aclImdb/train/neg/0_3.txt\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Positive Example:\n",
            "If you like adult comedy cartoons, like South Park, then this is nearly a \n",
            "similar format about the small adventures of three teenage girls at Bromwell \n",
            "High. Keisha, Natella and Latrina have given exploding sweets and behaved like \n",
            "bitches, I think Keisha is a good leader. There are also small stories going on \n",
            "with the teachers of the school. There's the idiotic principal, Mr. Bip, the \n",
            "nervous Maths teacher and many others. The cast is also fantastic, Lenny \n",
            "Henry's Gina Yashere, EastEnders Chrissie Watts, Tracy-Ann Oberman, Smack The \n",
            "Pony's Doon Mackichan, Dead Ringers' Mark Perry and Blunder's Nina Conti. I \n",
            "didn't know this came from Canada, but it is very good. Very good!\n",
            "\n",
            "Negative Example:\n",
            "Story of a man who has unnatural feelings for a pig. Starts out with a opening \n",
            "scene that is a terrific example of absurd comedy. A formal orchestra audience \n",
            "is turned into an insane, violent mob by the crazy chantings of it's singers. \n",
            "Unfortunately it stays absurd the WHOLE time with no general narrative \n",
            "eventually making it just too off putting. Even those from the era should be \n",
            "turned off. The cryptic dialogue would make Shakespeare seem easy to a third \n",
            "grader. On a technical level it's better than you might think with some good \n",
            "cinematography by future great Vilmos Zsigmond. Future stars Sally Kirkland and \n",
            "Frederic Forrest can be seen briefly."
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JWB-_MUBeEsw"
      },
      "source": [
        "## Pre-Processing\n",
        "\n",
        "We will need to prepare our datasets (train and test) so that we have enough information to construct our necessary features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIZSf67LpiJZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 836
        },
        "outputId": "5fafa678-8a5a-4069-a8a1-1a5c3add1b7c"
      },
      "source": [
        "!pip install spacy\n",
        "!python -m spacy download en"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: spacy in /usr/local/lib/python3.6/dist-packages (2.2.4)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (0.4.1)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy) (2.0.3)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (4.41.1)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.1.3)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.18.5)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (7.4.0)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.0.2)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (0.8.0)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (2.23.0)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.0.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy) (50.3.0)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy) (3.0.2)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.0.2)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2020.6.20)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy) (2.0.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy) (3.2.0)\n",
            "Requirement already satisfied: en_core_web_sm==2.2.5 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz#egg=en_core_web_sm==2.2.5 in /usr/local/lib/python3.6/dist-packages (2.2.5)\n",
            "Requirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.6/dist-packages (from en_core_web_sm==2.2.5) (2.2.4)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.0.3)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.2)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.18.5)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (7.4.0)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.1.3)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (4.41.1)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.2)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.2)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.8.0)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.23.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (50.3.0)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (2.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2020.6.20)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2.10)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.2.0)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_sm')\n",
            "\u001b[38;5;2m✔ Linking successful\u001b[0m\n",
            "/usr/local/lib/python3.6/dist-packages/en_core_web_sm -->\n",
            "/usr/local/lib/python3.6/dist-packages/spacy/data/en\n",
            "You can now load the model via spacy.load('en')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPIiYHWoeawv"
      },
      "source": [
        "import os\n",
        "import spacy\n",
        "import pickle as pkl\n",
        "\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# We don't need some of the features of the spacy pipeline, \n",
        "# this speeds up processing time\n",
        "# Only want to do: tokenization, lemmatization\n",
        "nlp = spacy.load('en', disable=['parser', 'ner', 'sentencizer', 'tagger'])\n",
        "\n",
        "def process_class(class_path, class_label):\n",
        "    \"\"\"\n",
        "    Processes the data for the specific class that we're working with.\n",
        "    We will generate additional features such as the tokenized words, lemmas.\n",
        "\n",
        "    We won't use all of the features, but this illustrates a typical pipeline\n",
        "    we would need to make for a task.\n",
        "    \"\"\"\n",
        "    examples = []\n",
        "\n",
        "    for file_name in tqdm(os.listdir(class_path)):\n",
        "        file_path = os.path.join(class_path, file_name)\n",
        "\n",
        "        with open(file_path, 'r') as example_file:\n",
        "            text = example_file.read()\n",
        "        doc = nlp(text)\n",
        "\n",
        "        tokens = [t.text for t in doc]\n",
        "        lemmas = [t.lemma for t in doc]\n",
        "\n",
        "        # doc.sents\n",
        "        # for sent in doc.sents:\n",
        "        #   # Do something with words\n",
        "        \n",
        "        examples.append({\n",
        "            'text': doc.text,\n",
        "            'tokens': tokens,\n",
        "            'lemmas': lemmas,\n",
        "            'class': class_label\n",
        "        })\n",
        "\n",
        "        # run, running, ran -> run\n",
        "\n",
        "    return examples\n",
        "\n",
        "\n",
        "def process_split(split_folder):\n",
        "    \"\"\"\n",
        "    Process all the examples in a given split\n",
        "    \"\"\"\n",
        "\n",
        "    # Process positives\n",
        "    pos_path = os.path.join(split_folder, 'pos')\n",
        "    positive_data = process_class(pos_path, 'pos')\n",
        "\n",
        "    # Process negatives\n",
        "    neg_path = os.path.join(split_folder, 'neg')\n",
        "    negative_data = process_class(neg_path, 'neg')\n",
        "    \n",
        "    return positive_data + negative_data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ybq7hVQr2vUg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "232c17ed-a4fc-45a3-9f33-53de3190e5de"
      },
      "source": [
        "# How spacy works\n",
        "\n",
        "text = 'Hello, my name is Rishi!'\n",
        "\n",
        "doc = nlp(text)\n",
        "\n",
        "print(doc)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hello, my name is Rishi!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CBpcCFxJrnJk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213,
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          ]
        },
        "outputId": "bbdf5276-898c-46ab-81bc-05d2c70c615f"
      },
      "source": [
        "\n",
        "dataset_path = 'aclImdb'\n",
        "\n",
        "splits = [\n",
        "         'train',\n",
        "         'test'\n",
        "]\n",
        "\n",
        "dataset = {}\n",
        "\n",
        "for split in splits:\n",
        "    split_folder = os.path.join(dataset_path, split)\n",
        "    split_data = process_split(split_folder)\n",
        "\n",
        "    dataset[split] = split_data\n",
        "\n",
        "# Save the processed data\n",
        "\n",
        "with open('processed_data.pkl', 'wb') as processed_dataset_file:\n",
        "    pkl.dump(dataset, processed_dataset_file)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, max=12500.0), HTML(value='')))"
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          "metadata": {
            "tags": []
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        {
          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        {
          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "model_id": "7ef892a70f3c4ce581bd50847d1a7247",
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fHLXCBet4p0A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1db6bb72-092c-44c8-f90b-16d44801f05e"
      },
      "source": [
        "dataset['train'][0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'class': 'pos',\n",
              " 'lemmas': [4690420944186131903,\n",
              "  2158845516055552166,\n",
              "  447765159362469301,\n",
              "  11268780503345276587,\n",
              "  9548244504980166557,\n",
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              "  17783898315335276456,\n",
              "  12646065887601541794],\n",
              " 'text': \"I did not set very high expectations for this movie, which left me pleasantly surprised. The story is a little strange sometimes but overall I think it has an acceptable credibility. The action scenes are rather nice and the accompanying music is used to induce a a bit of patriotic feelings common to US movies. This may not be the best movie ever but it's uncommon for Sweden and I hope to see more similar ones in the future.\",\n",
              " 'tokens': ['I',\n",
              "  'did',\n",
              "  'not',\n",
              "  'set',\n",
              "  'very',\n",
              "  'high',\n",
              "  'expectations',\n",
              "  'for',\n",
              "  'this',\n",
              "  'movie',\n",
              "  ',',\n",
              "  'which',\n",
              "  'left',\n",
              "  'me',\n",
              "  'pleasantly',\n",
              "  'surprised',\n",
              "  '.',\n",
              "  'The',\n",
              "  'story',\n",
              "  'is',\n",
              "  'a',\n",
              "  'little',\n",
              "  'strange',\n",
              "  'sometimes',\n",
              "  'but',\n",
              "  'overall',\n",
              "  'I',\n",
              "  'think',\n",
              "  'it',\n",
              "  'has',\n",
              "  'an',\n",
              "  'acceptable',\n",
              "  'credibility',\n",
              "  '.',\n",
              "  'The',\n",
              "  'action',\n",
              "  'scenes',\n",
              "  'are',\n",
              "  'rather',\n",
              "  'nice',\n",
              "  'and',\n",
              "  'the',\n",
              "  'accompanying',\n",
              "  'music',\n",
              "  'is',\n",
              "  'used',\n",
              "  'to',\n",
              "  'induce',\n",
              "  'a',\n",
              "  'a',\n",
              "  'bit',\n",
              "  'of',\n",
              "  'patriotic',\n",
              "  'feelings',\n",
              "  'common',\n",
              "  'to',\n",
              "  'US',\n",
              "  'movies',\n",
              "  '.',\n",
              "  'This',\n",
              "  'may',\n",
              "  'not',\n",
              "  'be',\n",
              "  'the',\n",
              "  'best',\n",
              "  'movie',\n",
              "  'ever',\n",
              "  'but',\n",
              "  'it',\n",
              "  \"'s\",\n",
              "  'uncommon',\n",
              "  'for',\n",
              "  'Sweden',\n",
              "  'and',\n",
              "  'I',\n",
              "  'hope',\n",
              "  'to',\n",
              "  'see',\n",
              "  'more',\n",
              "  'similar',\n",
              "  'ones',\n",
              "  'in',\n",
              "  'the',\n",
              "  'future',\n",
              "  '.']}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9Vu0t_ishhp"
      },
      "source": [
        "## Multinomial Naive Bayes\n",
        "\n",
        "The model we are going to explore is a multinomial Naive Bayes classifier with n-gram features. \n",
        "\n",
        "The Naive Bayes model will take the n-gram counts as input and creates a probability distribution for the n-grams in each class (positive or negative). For e.g. we would normally expect positive sentiment reviews to have phrases used such as \"very good\", \"amazing\" etc., appear more frequently while the negative sentiment reviews would have phrases like \"awful\", \"terrible\" used more frequently.\n",
        "\n",
        "Naive Bayes requires the counts of each n-gram as input in the form of a vector. Each index of the vector corresponds to an n-gram. Therefore, we will need to construct the feature vector out of our data. \n",
        "\n",
        "We will implement our own count vectorizer that return a vector of counts of 10,000 most-frequent n-grams in our dataset.\n",
        "\n",
        "Vocabulary = {'movie', 'good', 'bad', 'is'}\n",
        "NAIVE_BAYES = (1, 0, 1)\n",
        "\n",
        "TEXT = 'movie is bad'\n",
        "X = (1, 0, 1, 1)\n",
        "\n",
        "TEXT = 'movie is good'\n",
        "X = (1, 1, 0, 1)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M34EjF-LueoL"
      },
      "source": [
        "from collections import Counter\n",
        "import numpy as np\n",
        "\n",
        "class CountVectorizer(object):\n",
        "    def __init__(self, vocab_size=10000, bos='<s>', eos='</s>'):\n",
        "        self.vocab_size = vocab_size\n",
        "        self.bos = bos\n",
        "        self.eos = eos\n",
        "        self.ngram2idx = {}\n",
        "        self.idx2ngram = {}\n",
        "    \n",
        "    def fit(self, tokenized_examples):\n",
        "        # We will try to build a vectorizer of unigrams and bigrams\n",
        "        # from our tokens\n",
        "        n_gram_counter = Counter()\n",
        "\n",
        "        for example in tokenized_examples:\n",
        "            prev_token = self.bos\n",
        "            for token in example:\n",
        "                n_gram_counter[token] += 1\n",
        "                n_gram_counter[(prev_token, token)] += 1\n",
        "                prev_token = token\n",
        "            \n",
        "            n_gram_counter[(prev_token, self.eos)] += 1\n",
        "        \n",
        "        print(\"Number of unigrams and bigrams in corpus:\", len(n_gram_counter))\n",
        "        most_frequent_n_grams = n_gram_counter.most_common(self.vocab_size)\n",
        "\n",
        "        # Mapping the most frequent n-grams with an index\n",
        "        for i, (n_gram, _) in enumerate(most_frequent_n_grams):\n",
        "            self.ngram2idx[n_gram] = i\n",
        "            self.idx2ngram[i] = n_gram\n",
        "\n",
        "    def transform(self, tokenized_examples):\n",
        "\n",
        "        vectorized_counts = np.zeros((len(tokenized_examples), self.vocab_size))\n",
        "\n",
        "        for i, example in tqdm(enumerate(tokenized_examples)):\n",
        "            prev_token = self.bos\n",
        "            example_vector = vectorized_counts[i]\n",
        "            for token in example:\n",
        "\n",
        "                # If i see a particular unigram, I increment its count\n",
        "                unigram_idx = self.ngram2idx.get(token, -1)\n",
        "                if unigram_idx != -1:\n",
        "                    example_vector[unigram_idx] += 1\n",
        "\n",
        "                # If I see a particular bigram, I increment its count\n",
        "                bigram_idx = self.ngram2idx.get((prev_token, token), -1)\n",
        "                if bigram_idx != -1:\n",
        "                    example_vector[bigram_idx] += 1\n",
        "                prev_token = token\n",
        "            \n",
        "            bigram_idx = self.ngram2idx.get((prev_token, self.eos), -1)\n",
        "            if bigram_idx != -1:\n",
        "                example_vector[bigram_idx] += 1\n",
        "                \n",
        "\n",
        "        return vectorized_counts\n",
        "\n",
        "    def fit_transform(self, tokenized_examples):\n",
        "        self.fit(tokenized_examples)\n",
        "\n",
        "        return self.transform(tokenized_examples)\n",
        "\n",
        "    def describe_vector(self, example_vector):\n",
        "        bow_dict = {}\n",
        "        for j, count in enumerate(example_vector):\n",
        "            if count > 0:\n",
        "                bow_dict[self.idx2ngram[j]] = count\n",
        "        \n",
        "        return bow_dict"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yyaI_1B30rgE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132,
          "referenced_widgets": [
            "bc060952825f445f91e6c38d45010a51",
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        },
        "outputId": "5437d357-1be4-43a2-a5d7-2a102bde6c9d"
      },
      "source": [
        "train_examples = dataset['train']\n",
        "test_examples = dataset['test']\n",
        "\n",
        "# Now we need to convert the datasets into feature vectors\n",
        "train_tokenized_examples = [example['tokens'] for example in train_examples]\n",
        "test_tokenized_examples = [example['tokens'] for example in test_examples]\n",
        "\n",
        "vectorizer = CountVectorizer()\n",
        "\n",
        "X_train = vectorizer.fit_transform(train_tokenized_examples)\n",
        "y_train = [example['class'] for example in train_examples]\n",
        "X_test = vectorizer.transform(test_tokenized_examples)\n",
        "y_test = [example['class']for example in test_examples]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of unigrams and bigrams in corpus: 1558010\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0F9JdrYu3W91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2739faf5-a68f-45de-96c4-2065a1104011"
      },
      "source": [
        "from pprint import pprint\n",
        "from textwrap import fill\n",
        "\n",
        "print(\"Example:\\n\", fill(train_examples[0]['text']))\n",
        "pprint(vectorizer.describe_vector(X_train[0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Example:\n",
            " I did not set very high expectations for this movie, which left me\n",
            "pleasantly surprised. The story is a little strange sometimes but\n",
            "overall I think it has an acceptable credibility. The action scenes\n",
            "are rather nice and the accompanying music is used to induce a a bit\n",
            "of patriotic feelings common to US movies. This may not be the best\n",
            "movie ever but it's uncommon for Sweden and I hope to see more similar\n",
            "ones in the future.\n",
            "{\"'s\": 1.0,\n",
            " ',': 1.0,\n",
            " '.': 4.0,\n",
            " 'I': 3.0,\n",
            " 'The': 2.0,\n",
            " 'This': 1.0,\n",
            " 'US': 1.0,\n",
            " 'a': 3.0,\n",
            " 'acceptable': 1.0,\n",
            " 'action': 1.0,\n",
            " 'an': 1.0,\n",
            " 'and': 2.0,\n",
            " 'are': 1.0,\n",
            " 'be': 1.0,\n",
            " 'best': 1.0,\n",
            " 'bit': 1.0,\n",
            " 'but': 2.0,\n",
            " 'common': 1.0,\n",
            " 'credibility': 1.0,\n",
            " 'did': 1.0,\n",
            " 'ever': 1.0,\n",
            " 'expectations': 1.0,\n",
            " 'feelings': 1.0,\n",
            " 'for': 2.0,\n",
            " 'future': 1.0,\n",
            " 'has': 1.0,\n",
            " 'high': 1.0,\n",
            " 'hope': 1.0,\n",
            " 'in': 1.0,\n",
            " 'is': 2.0,\n",
            " 'it': 2.0,\n",
            " 'left': 1.0,\n",
            " 'little': 1.0,\n",
            " 'may': 1.0,\n",
            " 'me': 1.0,\n",
            " 'more': 1.0,\n",
            " 'movie': 2.0,\n",
            " 'movies': 1.0,\n",
            " 'music': 1.0,\n",
            " 'nice': 1.0,\n",
            " 'not': 2.0,\n",
            " 'of': 1.0,\n",
            " 'ones': 1.0,\n",
            " 'overall': 1.0,\n",
            " 'pleasantly': 1.0,\n",
            " 'rather': 1.0,\n",
            " 'scenes': 1.0,\n",
            " 'see': 1.0,\n",
            " 'set': 1.0,\n",
            " 'similar': 1.0,\n",
            " 'sometimes': 1.0,\n",
            " 'story': 1.0,\n",
            " 'strange': 1.0,\n",
            " 'surprised': 1.0,\n",
            " 'the': 3.0,\n",
            " 'think': 1.0,\n",
            " 'this': 1.0,\n",
            " 'to': 3.0,\n",
            " 'used': 1.0,\n",
            " 'very': 1.0,\n",
            " 'which': 1.0,\n",
            " (',', 'which'): 1.0,\n",
            " ('.', '</s>'): 1.0,\n",
            " ('.', 'The'): 2.0,\n",
            " ('.', 'This'): 1.0,\n",
            " ('<s>', 'I'): 1.0,\n",
            " ('I', 'did'): 1.0,\n",
            " ('I', 'hope'): 1.0,\n",
            " ('I', 'think'): 1.0,\n",
            " ('The', 'action'): 1.0,\n",
            " ('The', 'story'): 1.0,\n",
            " ('a', 'bit'): 1.0,\n",
            " ('a', 'little'): 1.0,\n",
            " ('action', 'scenes'): 1.0,\n",
            " ('and', 'I'): 1.0,\n",
            " ('and', 'the'): 1.0,\n",
            " ('be', 'the'): 1.0,\n",
            " ('best', 'movie'): 1.0,\n",
            " ('bit', 'of'): 1.0,\n",
            " ('but', 'it'): 1.0,\n",
            " ('did', 'not'): 1.0,\n",
            " ('for', 'this'): 1.0,\n",
            " ('future', '.'): 1.0,\n",
            " ('has', 'an'): 1.0,\n",
            " ('in', 'the'): 1.0,\n",
            " ('is', 'a'): 1.0,\n",
            " ('it', \"'s\"): 1.0,\n",
            " ('it', 'has'): 1.0,\n",
            " ('left', 'me'): 1.0,\n",
            " ('may', 'not'): 1.0,\n",
            " ('movie', ','): 1.0,\n",
            " ('movie', 'ever'): 1.0,\n",
            " ('movies', '.'): 1.0,\n",
            " ('music', 'is'): 1.0,\n",
            " ('not', 'be'): 1.0,\n",
            " ('scenes', 'are'): 1.0,\n",
            " ('story', 'is'): 1.0,\n",
            " ('the', 'best'): 1.0,\n",
            " ('the', 'future'): 1.0,\n",
            " ('think', 'it'): 1.0,\n",
            " ('this', 'movie'): 1.0,\n",
            " ('to', 'see'): 1.0,\n",
            " ('used', 'to'): 1.0}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KuufVz924RxX"
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "classifier = MultinomialNB()\n",
        "\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "predictions = classifier.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-0VQbcs5vkN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "08779115-266e-42f4-b11a-5eb5431cf8e5"
      },
      "source": [
        "print(test_tokenized_examples[0])\n",
        "print(y_test[0])\n",
        "print(predictions[0])\n",
        "\n",
        "print(len(predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Dylan', 'and', 'Bobby', 'are', 'boyhood', 'friends', 'and', 'they', 'are', 'in', 'love', 'the', 'way', 'that', 'young', 'boys', 'sometimes', 'are', '.', 'But', 'Dylan', 'has', 'met', 'a', 'girl', 'and', 'is', 'starting', 'to', 'put', 'those', 'boyhood', 'things', 'aside', '.', 'Bobby', 'knows', 'that', 'he', \"'s\", 'not', 'interested', 'in', 'girls', 'and', 'misses', 'what', 'he', 'had', 'with', 'Dylan', '.', '<', 'br', '/><br', '/>Told', 'as', 'part', 'cheesy', '80', \"'s\", 'pop', 'video', ',', 'part', 'home', 'movie', ',', 'part', 'video', 'recollection', 'this', 'film', 'tells', 'a', 'confusing', 'and', 'sad', ',', 'but', 'all', 'too', 'often', 'true', ',', 'story', 'that', 'will', 'hit', 'home', 'with', 'many', 'that', 'see', 'it', '.', '<', 'br', '/><br', '/>This', 'film', 'brings', 'back', 'a', 'lot', 'of', 'memories', 'and', 'struck', 'a', 'very', 'true', 'chord', 'with', 'me', 'but', 'I', 'wish', 'the', 'film', 'maker', 'had', 'gone', 'a', 'bit', 'further', 'and', 'left', 'it', 'on', 'a', 'happier', 'note', '.', 'Yes', ',', 'we', 'all', 'love', 'and', 'lose', ',', 'even', 'when', 'we', 'are', 'young', ',', 'but', 'there', \"'s\", 'always', 'tomorrow', ',', 'especially', 'when', 'we', 'are', 'young', '.']\n",
            "pos\n",
            "pos\n",
            "25000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oChxxOXL4prt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4fd65d20-52ad-4417-ed92-73b86edd6840"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "print(\"Accuracy: \", accuracy_score(y_test, predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  0.82736\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JghwuxV6Peb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c0ba62af-6c39-45ad-9df8-6dcb6d372d0a"
      },
      "source": [
        "# Let us manually inspect some examples\n",
        "\n",
        "indices = np.random.choice(list(range(len(test_tokenized_examples))), size=20)\n",
        "\n",
        "for i, example_index in enumerate(indices):\n",
        "    example = test_examples[example_index]\n",
        "    print(\"Example\", i + 1)\n",
        "    print(\"Text:\")\n",
        "    print(fill(example['text']))\n",
        "\n",
        "    print(\"True Label:\", example['class'])\n",
        "    print(\"Predicted Label:\", classifier.predict([X_test[example_index]])[0])\n",
        "    print(\"Prediction probability: \", classifier.predict_proba([X_test[example_index]])[0])\n",
        "    print()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Example 1\n",
            "Text:\n",
            "How important is the director, anyway? In this film, made in the\n",
            "politically tumultuous times of the late 60s where questions of social\n",
            "organization were prime conflicts, asks that question by making a\n",
            "movie that turns the camera away from the action and only begs to\n",
            "reveal the director, William Greaves. It is an important work, as it\n",
            "shows like no other movie shows the difficulties in blocking,\n",
            "organizing, and setting the scene; it reveals the role of the crew,\n",
            "something most directors frankly would like to disappear completely\n",
            "and that the invisibility of is essential for suspending disbelief;\n",
            "and it also puts into consideration the role of performance and\n",
            "scripting and how they match/don't match reality and what that has to\n",
            "say about how the director ultimately influences reality (if at\n",
            "all).<br /><br />The documentary, or pseudo-documentary, or fictional\n",
            "narrative (whichever you prefer, via your interpretations of the\n",
            "themes) has its brain in the over-educated, over-intellectual crew,\n",
            "its guts in the lost performers struggling to understand the vague and\n",
            "ambiguous directions, and its heart in the director, who stands in as\n",
            "the desire to portray, to represent, to express without any idea how\n",
            "to do any of those things or why he wants to do it. It's a film that\n",
            "purposefully repeats banalities just to see if they can become more\n",
            "than banalities. It's a film that sometimes shows the multiple shots\n",
            "simultaneously, just to leave the editing to the audience and also\n",
            "reveal how disturbingly different shots change perspective.<br /><br\n",
            "/>It's an important work, and something that everyone interested in\n",
            "the industry and process of film-making should watch and understand.\n",
            "It, like many experimental films, has no real mass-audience appeal--\n",
            "it's not for them. It's for the industry, and its for the 60s, asking\n",
            "what to do with a group-effort medium that still relies on a single\n",
            "\"voice\" and \"author\".<br /><br />--PolarisDiB\n",
            "True Label: pos\n",
            "Predicted Label: pos\n",
            "Prediction probability:  [8.91576471e-10 9.99999999e-01]\n",
            "\n",
            "Example 2\n",
            "Text:\n",
            "I get a kick out of the new Who fans who call it, excellent,Their\n",
            "entitled to that opinion but the new series isn't quite there yet,\n",
            "it's getting there. It's definitely good.<br /><br />First the good\n",
            "things. The special effects are obviously better nearly 20 yrs later\n",
            "since the last episode of Doctor Who \"Battlefield\" was aired.<br /><br\n",
            "/>One thing the BBC has always liked is Doctor Who (except when they\n",
            "tried to cancel in the 70's and 80's). However, the anticipation and\n",
            "the pressure was great but I think the New series has passed the test\n",
            "it's good, it's still not excellent because it does have it's flaws.\n",
            "Some of the stories \"Dalek\" were very poor. I assume it was written in\n",
            "a couple of minutes by some idiot who never bothered to remake the\n",
            "Dalek character or never bothered to watch the old series.<br /><br\n",
            "/>\"Aliens in London\" was by far probably the best episode so far, it\n",
            "started a new storyline about Aliens which has lasted across several\n",
            "episodes including \"Boomtown\".<br /><br />Now the other bad things,\n",
            "the companion, Rose, Billie Piper, isn't great. She's actually quite\n",
            "annoying but as is Dr. Who a young teeny bopper had to be eventually\n",
            "chosen and she got the part.<br /><br />Christopher Eccleston is\n",
            "pretty respectable, he's started to catch his niche. He tried to play\n",
            "the character, straight then funny always missing the right punchline\n",
            "but he's taken it much more serious and it shows.<br /><br\n",
            "/>Continuity wise there still some issues that didn't get resolved\n",
            "well, Paul Mcgann was still the doctor before this but he didn't get\n",
            "put back.<br /><br />Aside from that though the series overall is\n",
            "pretty good, I haven't missed an episode and it never gets boring, so\n",
            "I recommend any Doctor Who fan to check it out and see the new Doctor\n",
            "Who series.\n",
            "True Label: pos\n",
            "Predicted Label: pos\n",
            "Prediction probability:  [6.66021178e-04 9.99333979e-01]\n",
            "\n",
            "Example 3\n",
            "Text:\n",
            "First of all, I just have to say that I'm a huge LOST fan. Everyone\n",
            "who makes the show, I love 'em. It's got everything in it, really.<br\n",
            "/><br />I'm glad that they have great people making it, for instance\n",
            "Jack Bender is the greatest director, well, maybe after J.J. Abrams.\n",
            "And of course, full credit to Damon Lindelof & Carlton Cuse. They're\n",
            "geniuses.<br /><br />Anyway, the season 2 finale is definitely one of\n",
            "the best episodes of LOST ever!!! Live Together, Die Alone focuses on\n",
            "Desmond David Hume's story of getting to the island, and Henry Ian\n",
            "Cusick is so great playing that character! The makers of the show\n",
            "would have been very stupid if they would've let old'Des die on us.<br\n",
            "/><br />So, in LOST, the plane crash survivors have been there for 65\n",
            "days.. 65 days, man. Whileas Rousseau has been there for SIXTEEN\n",
            "years, and the Others have been there for God knows how long.\n",
            "Referring to Jeffrey Lieber, Lindelof & Abrams. Anyway, I hope we'll\n",
            "get the answers we need, in season 3. Can't wait! My vote is nine.\n",
            "True Label: pos\n",
            "Predicted Label: pos\n",
            "Prediction probability:  [0.01703259 0.98296741]\n",
            "\n",
            "Example 4\n",
            "Text:\n",
            "The great Yul Brynner, who won an 'Oscar', and who has starred\n",
            "blockbusters such as 'The Ten Commandments' among lots of others,\n",
            "ended his remarkable career with cheap backlot movies such as this\n",
            "one, 'Sartana', and such. Regretable, indeed. One should take pity on\n",
            "seeing him making his very best to make this idiotic thing stand. Gone\n",
            "were the days when he was surrounded by Steve McQueen, James Coburn,\n",
            "Charles Bronson, Eli Wallach, in 'The Magnificent Seven', and walked\n",
            "around under the famous Elmer Bernstein soundtrack. It's difficult to\n",
            "make a living out of being an actor, sometimes.\n",
            "True Label: neg\n",
            "Predicted Label: pos\n",
            "Prediction probability:  [8.86208323e-06 9.99991138e-01]\n",
            "\n",
            "Example 5\n",
            "Text:\n",
            "What a great movie this is. I found it full of the delightfully\n",
            "unexpected pain of being a single father of a teenage girl. And it is\n",
            "set in a tropical island 'paradise'as well. <br /><br />Gerard\n",
            "Depardeiux brings his special European flair to this story about a\n",
            "divorced father of a teenage girl. They are on holiday together and\n",
            "she begins to add to the excitement on the island in many unexpected\n",
            "ways. But you will need to see the film for yourself to see all the\n",
            "hilarious situations they find for themselves.<br /><br />There are a\n",
            "few cult classics which all teens should see. This movie should be\n",
            "added to the list. In addition to Dirty Dancing, Ferris Bueller's Day\n",
            "Off, Bill and Ted's Excellent Adventure, Rocky Horror Picture Show and\n",
            "Animal House, My Father the Hero should be required film study. Watch\n",
            "it as if you had a teenage daughter and you'll be rolling with\n",
            "laughter. Watch it with your teenage daughter and prepare to be\n",
            "laughed at for months.\n",
            "True Label: pos\n",
            "Predicted Label: pos\n",
            "Prediction probability:  [1.83814086e-07 9.99999816e-01]\n",
            "\n",
            "Example 6\n",
            "Text:\n",
            "Forget the fact that most people expect an action movie form Seagal.\n",
            "This movie simply had no substance. Very long, drawn out, and boring.\n",
            "Scenes were much longer than they had to be - the camera would often\n",
            "focus on something for long periods of time when absolutely nothing\n",
            "was happening. Cure for insomnia. Worst Seagal movie by far (I do like\n",
            "some of his other movies). 1/10\n",
            "True Label: neg\n",
            "Predicted Label: neg\n",
            "Prediction probability:  [1.00000000e+00 2.14656778e-10]\n",
            "\n",
            "Example 7\n",
            "Text:\n",
            "Oh, my. Poor Jane must have done the old rolling-over-in-the-grave\n",
            "thing. Even allowing for poor production values for the time (1971)\n",
            "and the format (some kind of mini-series), this is baaaaaad. Whatever\n",
            "else you do with Austen, the dialog should sparkle (even in this,\n",
            "perhaps her most serious work), and melodrama should be strictly out\n",
            "of bounds. Alas, not the case with this production. By the time you\n",
            "get to Anne's \"Frederick, Frederick, Frederick,\" you'll either be\n",
            "laughing or crying. Unless you're just out to visually \"collect\" all\n",
            "extant films of Austen's work, you can skip this one. If you do watch\n",
            "it, however, there are small consolations: The actresses playing\n",
            "Anne's sisters each do a wonderful job with their roles.\n",
            "True Label: neg\n",
            "Predicted Label: neg\n",
            "Prediction probability:  [0.99857277 0.00142723]\n",
            "\n",
            "Example 8\n",
            "Text:\n",
            "I could have liked this if I didn't like Diane Arbus.<br /><br\n",
            "/>Didn't really capture Arbus unique visual aesthetic(as Stanley\n",
            "Kubrick did in \"The Shinning\", with the twin girls in the hallway, a\n",
            "direct homage to one of Arbus most famous photos, and one with\n",
            "haunting qualities none of this movies images have), and certainly\n",
            "none of her character(outgoing), likeness(thin and Jewish, not wasp\n",
            "Queen Kidman), or life, save some very superficial similarities, she\n",
            "was a fashion who later photographed \"freaks\" and \"outsiders\", among\n",
            "many things. This film focused on the \"freak\", as her symbol for\n",
            "artistic and personal empowerment, in a really shallow and predictable\n",
            "fashion. Outside of that, it might have been an interesting movie\n",
            "about middle class fetish, but I was too annoyed to care.<br /><br\n",
            "/>Perhaps if Arbus name had been left off, this might have been less\n",
            "annoying, but had it not had her name in the title there would have\n",
            "been nothing to make me interested in seeing in this in the first\n",
            "place. A letdown from \"Secretary\".\n",
            "True Label: neg\n",
            "Predicted Label: neg\n",
            "Prediction probability:  [9.99119378e-01 8.80621764e-04]\n",
            "\n",
            "Example 9\n",
            "Text:\n",
            "After sitting through the trainwreck that was the first Dark Harvest\n",
            "movie, I couldn't leave bad enough alone. Upon seeing that there was a\n",
            "sequel (or rather what I believed to be a sequel)I had to increase my\n",
            "pain level. Seeing that this had nothing to do with Dark Harvest, that\n",
            "should have been a good thing. We didn't get any killer scarecrows in\n",
            "this one, instead we got a jackass walking around a cornfield\n",
            "screaming out little things like, \"Girls!\" and \"Can you hear me?\"\n",
            "every so often. Plus we got two (four if you include the two girls\n",
            "that the director wanted the same effect as the twins in The Shining)\n",
            "obnoxious little girls who couldn't act. And the cherry on top of this\n",
            "mess would have to be the Corn Cop. I should have known this movie was\n",
            "going to be terrible when the dog got an opening credit. How I managed\n",
            "to stay awake through this movie, I'll never know.\n",
            "True Label: neg\n",
            "Predicted Label: neg\n",
            "Prediction probability:  [1.00000000e+00 3.70677403e-21]\n",
            "\n",
            "Example 10\n",
            "Text:\n",
            "PLOT SPOILERS!!!! Dr. Boch (George C. Scott) is the chief of medicine\n",
            "at a major NYC hospital. He's left his wife, his children have\n",
            "disowned him, he's impotent, drinks a lot and contemplates suicide.\n",
            "Also there's a killer roaming the hospital. Then he meets VERY strange\n",
            "Barbara (Diana Rigg) and falls in love. She wants him to run away with\n",
            "her--but can he completely give up on his old life and start a new\n",
            "one?<br /><br />Very strange movie with an Oscar winning script by\n",
            "Paddy Chayefsky. It presents a suicidal main character and shows us a\n",
            "hospital full of overworked nurses and doctors that is run\n",
            "incompetently. It manages (somehow) to actually make this seem pretty\n",
            "funny. It's not laugh out loud humor--it's VERY black humor. Also the\n",
            "acting is right on target--Scott is just great (and Oscar nominated)\n",
            "here. You see him trying to keep his sanity in a totally crazy\n",
            "situation. Riggs character is more than a little odd but her matter of\n",
            "fact manner works and she's also incredibly beautiful. The script is\n",
            "strong and brutal--but never too much. I think it fumbles the ball at\n",
            "the end with a situation that goes way too over the top--but it's\n",
            "still worth seeing. If this had been done totally seriously it\n",
            "probably would be impossible to take. Also look for Katherine Helmond\n",
            "in a small bit and Stockard Channing and Christopher Guest in\n",
            "uncredited bits. I personally had trouble taking this seriously. From\n",
            "what I've heard hospitals WERE this bad back in the 1970s but not\n",
            "anymore. See it for the acting and script. I give it a 7.\n",
            "True Label: pos\n",
            "Predicted Label: pos\n",
            "Prediction probability:  [1.61944293e-06 9.99998381e-01]\n",
            "\n",
            "Example 11\n",
            "Text:\n",
            "I just thought I would add another observation, here. While there are\n",
            "a couple of visual sub-references, in this episode, to the possibility\n",
            "of unexpressed feelings between Jim Kirk and Yeoman Janice Rand; there\n",
            "is a special, physically tender, moment, toward the end. When the\n",
            "Enterprise is reversing at emergency warp speed in an attempt to\n",
            "outrun a possibly fatal Romulan plasma ball, Janice, perhaps fearing\n",
            "that their life is about to come to a dramatic end, seeks comfort by\n",
            "placing her head on Jim's sympathetic shoulder as they observe the\n",
            "aproaching instrument of their impending doom on the main viewer. I\n",
            "thought it was sweet (and Janice, of course, is gorgeous!).<br /><br\n",
            "/>(P.S. Goof:- Several times, while supposedly firing phasors, the\n",
            "film shows photon torpedoes being launched)\n",
            "True Label: pos\n",
            "Predicted Label: pos\n",
            "Prediction probability:  [4.52302475e-05 9.99954770e-01]\n",
            "\n",
            "Example 12\n",
            "Text:\n",
            "I haven't read the Anne Rice novel that this movie was based on, but\n",
            "who knows, maybe reading the book is cheaper than renting QUEEN OF THE\n",
            "DAMNED and is probably better for your health. It isn't that this\n",
            "movie is necessarily bad for your health, but a book can be very\n",
            "relaxing and certainly exercises the active part of your brain more so\n",
            "than this movie. You can count the number of pages by Anne Rice that\n",
            "I've read on one hand, but after seeing this movie and Interview with\n",
            "a Vampire, I get the feeling that she writes really good novels. The\n",
            "plots for both movies hint at a whole sea of deep and interwoven\n",
            "vampire history.<br /><br />Still, Stuart Townsend's voice-over\n",
            "narration gets a heck of a lot more annoying than Brad Pitt's vampire\n",
            "narrative ever did, and you can tell that QUEEN OF THE DAMNED's\n",
            "limited production resources barely give enough flesh to the Anne Rice\n",
            "storyline. While Interview decided to go with lace and elegance, QUEEN\n",
            "relies on low budget special effects that try really hard to be taken\n",
            "seriously. One can see that the original novel had potential as a\n",
            "movie and that the production team focused its attention in the wrong\n",
            "places. The costumes and rock & roll stage could have been replaced\n",
            "with more blood and an eerier soundtrack.<br /><br />However, I'll\n",
            "give credit where credit is due. The soundtrack is excellent. Korn and\n",
            "Disturbed had me down with the sickness bobbing my noggin like\n",
            "Butthead.<br /><br />The film opens with a very cool Goth-rock zoom &\n",
            "splice montage, but after the first ten minutes or so, the directing\n",
            "degenerates quickly. It's as if the movie was so long that the\n",
            "director realized that there wasn't enough time and enough money to do\n",
            "an Anne Rice novel justice. What results are some mediocre vampire\n",
            "scenes and plenty of cheesy special effects. Unfortunately, QUEEN OF\n",
            "THE DAMNED fails to do the genre justice just as its John Carpenter\n",
            "counterparts fail to impress. Where are the yellow contacts? Where's\n",
            "the pale blue make-up? Scene after scene, I shook my head reminiscing\n",
            "about the days of Salem's Lot and Fright Night when low budget was\n",
            "done right.<br /><br />There are redeeming qualities though that save\n",
            "this movie from being garbage. Props to Aaliyah, and may her soul\n",
            "forever rest in peace. She might have become a renowned actress, had\n",
            "her life not been taken from us so prematurely, for she did give this\n",
            "movie a decent performance with plenty of nice belly dancing. Did I\n",
            "mention that the soundtrack was good? Let's see, what else can I say?\n",
            "It wasn't too long. The Anne Rice novel could have easily been a three\n",
            "hour movie if an ambitious director like Francis Ford Coppola got his\n",
            "hands on it. There are a few twists and turns here and there in the\n",
            "plot. But all in all it was a legitimate rock and roll addition to the\n",
            "slew of second-rate vampire movies out there. The director of this\n",
            "movie went on to direct a new Battlestar Galactica mini series if that\n",
            "tells you anything.<br /><br />JY<br /><br />Jimboduck-dot-com\n",
            "True Label: pos\n",
            "Predicted Label: neg\n",
            "Prediction probability:  [1.00000000e+00 5.71516507e-29]\n",
            "\n",
            "Example 13\n",
            "Text:\n",
            "No one can argue with it. This IS and WILL BE the best movie ever, as\n",
            "it is the perfect definition of what any movie should be : a\n",
            "collective hypnosis beyond times. No movie can give you more perfectly\n",
            "this impression that you carried it inside you, even before you saw it\n",
            "for the first time.<br /><br />There are images that stay forever...\n",
            "True Label: pos\n",
            "Predicted Label: pos\n",
            "Prediction probability:  [0.0563851 0.9436149]\n",
            "\n",
            "Example 14\n",
            "Text:\n",
            "This movie was a very good Universal Monster movie. It once again\n",
            "stars Lon Chaney as The Wolfman and Glenn Strange as Frankenstein's\n",
            "Monster. Oh yeah, that jerk John Carradine is back again as Dracula. I\n",
            "like every actor in this movie. I especially liked Onslow Stevens as\n",
            "Dr. Edelmann. (It's spelled with 2 n's) I thought it was a good idea\n",
            "to have the goodhearted doctor himself doomed like Talbot was. One\n",
            "scene that I think is very good is the scene when Dr. Edelmann is in\n",
            "deep thought as he changes. Everything that is troubling him flashes\n",
            "before your eyes. The good Doctor is saying no while his evil side is\n",
            "saying yes. That's the only reason why I didn't want the Dracula\n",
            "character eliminated completely from this movie. I thought Dracula had\n",
            "no business in House of Frankenstein. If his character was taken out\n",
            "you wouldn't miss him. In this film the doctor's blood is contaminated\n",
            "with Dracula's, giving him his Jekyll and Hyde curse. I hate John\n",
            "Carradine and I don't think he should have ever played Dracula. I\n",
            "didn't mind other actors playing the Frankenstein Monster after the\n",
            "great Karloff because they all did good jobs. But when they get\n",
            "another actor to play Dracula it stops right there. John Carradine\n",
            "thought he was so high and mighty. They offered him the role of the\n",
            "Frankenstein Monster once and he turned it down because he thought he\n",
            "was too highly trained. I could just picture Carradine if he did play\n",
            "The Monster arguing with the director on the set, \"I don't have to\n",
            "take this from you, I've done Shakespeare.\" John Carradine wasn't\n",
            "Dracula and he never will be. Sorry John, Bela Lugosi is the one and\n",
            "only Dracula. Thank God they got Bela to come back as Dracula for\n",
            "Abbott and Costello Meet Frankenstein. <br /><br />Don't miss this\n",
            "movie. It doesn't disappoint you and you will enjoy it as you did the\n",
            "other. This is a very good addition to the monster movies. If you're a\n",
            "collector, be sure not to leave this one out.<br /><br />\n",
            "True Label: pos\n",
            "Predicted Label: neg\n",
            "Prediction probability:  [9.99999998e-01 1.81805009e-09]\n",
            "\n",
            "Example 15\n",
            "Text:\n",
            "This documentary about the life and comedy of Bill Hicks features bits\n",
            "from Hicks' \"Revelations\" and other stand-up gigs. It also features\n",
            "interviews with fellow comedians and people in the industry who knew\n",
            "him, as well as reporters and journalists who talk about how his\n",
            "political commentary was raw and brutal.<br /><br />I enjoyed it very\n",
            "much. I had already seen \"Revelations\" but the comedy clips were still\n",
            "refreshing. It's a nice balance of comedy and documentary that will\n",
            "explain Hicks' popularity to non-fans and please those who are already\n",
            "familiar with him.<br /><br />I rated it a ten.\n",
            "True Label: pos\n",
            "Predicted Label: pos\n",
            "Prediction probability:  [4.39038815e-10 1.00000000e+00]\n",
            "\n",
            "Example 16\n",
            "Text:\n",
            "I thought the movie was sub-par. The acting was good but not great,\n",
            "the story was funny but did not come out that way. The director\n",
            "dropped the ball on this movie. It was not James (jim) or Tea. IMHO it\n",
            "was the music that killed it. There is a scene where things go down\n",
            "hill and Jonny Cash music is playing - man was that depressing (not\n",
            "funny) killed my mood. After that the movie could not recover. The\n",
            "deportation scene had potential funny situation, good acting good set\n",
            "up - I even smirked but the music again was unsuited to the scene. The\n",
            "music kept me from being pulled in to the movie.<br /><br />I say it\n",
            "had potential but was poorly done, i would even say rushed into final\n",
            "production. Kind of reminiscent of the prequel to the exorcist: the\n",
            "beginning. The theater release was good, I though so after watching\n",
            "it, but the movie release exorcist:dominion was a helluva lot better.\n",
            "Same story just different director. Same should be done here.\n",
            "True Label: neg\n",
            "Predicted Label: neg\n",
            "Prediction probability:  [1.00000000e+00 8.62905205e-11]\n",
            "\n",
            "Example 17\n",
            "Text:\n",
            "This ranks as my favorite movie of all time. It's the best spoof of a\n",
            "science fiction movie ever; the fact that it was a sendup of Star Wars\n",
            "just made it all the better.<br /><br />I love slapstick. Think of\n",
            "this as the Marx brothers or the Three Stooges meet Star Wars. The\n",
            "writing is hilarious. The effects are a hoot. The free association\n",
            "that goes on guarantees all sorts of things coming out of left field.\n",
            "(I almost wet my pants when the Wookie Monster accosted the\n",
            "Princess.)<br /><br />Space Balls was a much longer movie, but only\n",
            "had about 15 minutes of good material in it, and I felt sort of ripped\n",
            "off afterwards, like buying a burger that turned out to be mostly\n",
            "filler. Hardware Wars, despite being only about 15 minutes long, would\n",
            "be worth paying a feature price, IMO.\n",
            "True Label: pos\n",
            "Predicted Label: neg\n",
            "Prediction probability:  [9.99439925e-01 5.60075093e-04]\n",
            "\n",
            "Example 18\n",
            "Text:\n",
            "My website (www.theflickguy.org) lists this pick as the worst movie of\n",
            "all time. Here is an excerpt: <br /><br />\"If I were strapped down to\n",
            "a chair and forced to watch this movie over and over again, I couldn't\n",
            "imagine Hell being any worse. Jim Varney plays a three-handed crazy\n",
            "guy bent on destroying the world (apparently starting with cinema).\n",
            "Now let's face it, no one expects a whole lot from a Varney movie, but\n",
            "this agonizing drivel had me dry-heaving for 92 minutes. Not a laugh.\n",
            "Not one. This is not kamp or gitchy, this is not even mindless. It is\n",
            "evil. Do not rent this, it may destroy your DVD player. Do not even\n",
            "buy the VHS from a 29-cent clearance bin to use as a blank tape. It is\n",
            "the worst film of all time. Period. I mean it. Really.\"\n",
            "True Label: neg\n",
            "Predicted Label: neg\n",
            "Prediction probability:  [1.00000000e+00 8.67497442e-18]\n",
            "\n",
            "Example 19\n",
            "Text:\n",
            "If you're tired by the same repetitive, unintelligent material that\n",
            "the mainstream movie industry releases, you'll enjoy \"You Are Alone\".\n",
            "It is thought provoking, well shot and riveting.<br /><br />Without\n",
            "revealing anything that you don't find out in the first few minutes of\n",
            "the movie, this is the story of a young white high school girl from an\n",
            "upper middle class environment who is working as an escort and is\n",
            "discovered by her neighbor. The vast majority of the movie occurs in a\n",
            "hotel room where he hired her to come.<br /><br />Through their\n",
            "discussion, you explore two shifting views of prostitution,\n",
            "depression, loneliness. Yet the movie is not depressing. It talks\n",
            "about dark things without being depressing.<br /><br />As a viewer,\n",
            "your emotions and preconceived notions are moved around, but gently.\n",
            "You come out of it with a lot to think about. I like that in a movie.\n",
            "True Label: pos\n",
            "Predicted Label: pos\n",
            "Prediction probability:  [4.08015115e-04 9.99591985e-01]\n",
            "\n",
            "Example 20\n",
            "Text:\n",
            "This is the worst movie I have ever seen. The story line is a joke,\n",
            "the effects are terrible, the cinematography doesn't fit the tone of\n",
            "the movie, the dialogue is cheesy, and the actors do a good job at\n",
            "screwing up the rest. People just don't act that way in real life\n",
            "situations. My question is: Who would fund such crap?<br /><br />The\n",
            "movie starts where some miners fall down a mine shaft after a fireman\n",
            "fails to save them. Next we join some bikers in a forest who ride\n",
            "around doing stunts on their bikes. One guy falls and breaks his leg\n",
            "or something. The fireman arrives to help them. Meanwhile, somebody\n",
            "starts a fire. Some more bike stunts. Bla bla bla.<br /><br />I wasted\n",
            "my time.<br /><br />Do not watch this movie.\n",
            "True Label: neg\n",
            "Predicted Label: neg\n",
            "Prediction probability:  [1.00000000e+00 1.02872178e-13]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DrBYZ5n371E8"
      },
      "source": [
        "### Is word morphology important?\n",
        "\n",
        "One question that we'd like to address is whether word morphology plays a role in our predictions for a Naive Bayes model. For that, we can construct a model trained on just the lemmas of the word and compare it with the performance of the original model we trained."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9O_subx8LgG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 149,
          "referenced_widgets": [
            "6b24a657965349929f853bb410e1f6e7",
            "1253929a11154277af80a36e054882d6",
            "6aece11653c04c01b420e9c59b85c8e5",
            "e70d0f3a4f8e4656a3652df3dfaf8302",
            "c963359224af40a0a2fbfa7b5c023432",
            "a7edb085b9af4f9887bb0ea2503cd9fe",
            "d99c3bb022b64fd79f9697c9207f57c4",
            "e1ee6f7e3d924a96a67cd4b6659034f7",
            "26b2c2045c284d6783c12b668004f4cc",
            "0437a91ad7044e3ea31743ee7d949cbd",
            "93ff9061fd584ebc826354aea5d2d2dc",
            "ecacd5459999489da524150bbad2d8c2",
            "ff8195e7d2c047529506017b696dd8ef",
            "9803f343ca97428299c9ee820b298c25",
            "6625abc5978f43b9a5a14639b27d7f8c",
            "a53061287caa4720bee70f27af94fe71"
          ]
        },
        "outputId": "4ca5238f-04f7-42b9-dafe-61ac850f4f75"
      },
      "source": [
        "train_lemmatized_examples = [example['lemmas'] for example in train_examples]\n",
        "test_lemmatized_examples = [example['lemmas'] for example in test_examples]\n",
        "\n",
        "lemma_vectorizer = CountVectorizer()\n",
        "\n",
        "X_lemma_train = lemma_vectorizer.fit_transform(train_lemmatized_examples)\n",
        "X_lemma_test = lemma_vectorizer.transform(test_lemmatized_examples)\n",
        "\n",
        "lemma_classifier = MultinomialNB()\n",
        "lemma_classifier.fit(X_lemma_train, y_train)\n",
        "\n",
        "lemma_predictions = classifier.predict(X_lemma_test)\n",
        "\n",
        "print(\"Accuracy: \", accuracy_score(y_test, lemma_predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of unigrams and bigrams in corpus: 1343429\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6b24a657965349929f853bb410e1f6e7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "26b2c2045c284d6783c12b668004f4cc",
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              "version_major": 2
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy:  0.51204\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVSF8_Ba9iXD"
      },
      "source": [
        "\n",
        "with open('nb_model_1.pkl', 'wb') as naive_bayes_model_1:\n",
        "    pkl.dump({\n",
        "        'vectorizer': vectorizer,\n",
        "        'classifier': classifier\n",
        "    }, naive_bayes_model_1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "do06o5dQ82Rq"
      },
      "source": [
        "From the above example, we have a significantly lower accuracy for our model when we simply use the word lemmas instead of the actual words."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14zy5YtE-RPG"
      },
      "source": [
        "## Exercises to try out and points to ponder\n",
        "1. Describe the Naives Bayes model mathematically. How do you construct the model from the data and make predictions using it?\n",
        "1. What are the assumptions of the Naive Bayes model. Does our data actually obey those assumptions?\n",
        "1. Add more metrics that measure class-specific performance and the behavior of the model.\n",
        "    1. How often does the model predict each class label?\n",
        "    1. For which one does the model perform better/worse?\n",
        "    1. Try to do some error analysis of the model.\n",
        "1. Play around with the vocabulary size (10 words, 100 words, 1k words, 10k words) and see how accuracy changes with vocabulary size. What is the trade-off that we are doing by varying vocabulary size?\n",
        "1. The question above asks us to test different values of our feature size, which is a form of hyperparameter tuning. How can you automate this process using SciKit-learn to get the best set of values?\n",
        "1. Sometimes, review writers may use rare synonyms of meaningful words or may misspell words. This is especially common in real data, and this may lead to our model to not perform as well. Can you modify the code to handle these cases?\n",
        "1. Try implementing n-gram TF-IDF features and use the model. Does it perform better or worse?\n",
        "1. Create a command-line tool or web application that accepts text input and returns the sentiment predicted by our model. What would the design of your application look like? What are the components for processing the text, creating the features and predicting the output?\n",
        "1. Try to implement your own MultinomialNB class. What are some of implementation issues you should consider and how will you handle them? Here are a few:\n",
        "    1. Many words are incredibly rare - the probability of some words would be in the order of $10^{-6}$. Naive Bayes involves multiplying several of these probabilities, so the probability of the text could be something like $10^{-200}$, which is not representable by floating point values. How would you handle this?\n",
        "    2. Writing for-loops that iterates over n-grams may be very slow, how can you optimize this?\n",
        "1. In the Naive Bayes model, some words may occur rarely in one class and may be more common in the other class. These are usually the most informative words for our classification. \n",
        "    1. The odds of the words would be either very large or close to 0. \n",
        "    1. That is, the ratio $ \\frac{p(w | pos)}{p(w | neg)} $ would be very close to infinity or 0 due to the numerator or denominator being the larger quantity. \n",
        "    1. If they are equally likely in both classes, then the ratio will be closer to 1.\n",
        "    1. Look at the attributes of the MultinomialNB class, and see which ones can be used to obtain this metric.\n",
        "    1. Do the words you see from this analysis  make intuitive sense? If some words don't make sense, they could be a source of error. Identify such cases in your dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uf25iAr_bxpZ"
      },
      "source": [
        "## References\n",
        "\n",
        "1. Building a sentiment classifier using SciKit-Learn (Dorian Lazar): https://towardsdatascience.com/building-a-sentiment-classifier-using-scikit-learn-54c8e7c5d2f0\n",
        "1. Learning Word Vectors for Sentiment Analysis (Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher, 2011) (<a>https://ai.stanford.edu/~amaas/papers/wvSent_acl2011.pdf</a>)"
      ]
    }
  ]
}