Why we do this?
Some time ago, I saw some news about GameStop's stock price on the news. I learned that GameStop's stock price suddenly rose due to the actions of people on a Reddit called Wallstreetbet, which aroused my great interest. So, I came up with the idea of using programs to analyze stock data directly related to people's emotions
I started out with the idea of aggregating what people were Posting on Reddit, and then doing sentiment analysis, then eventually creating a graph that has stock data, sentiment data, word clouds, and so on. I want to be able to find a correlation between sentiment data and stock prices or prove that they don't
NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.
A fast, powerful, flexible and easy to use open source data analysis and manipulation tool
A library helps me to handle Reddit API like this
import praw
reddit = praw.Reddit(
client_id="My client id",
client_secret="My client secret",
user_agent="my user agent"
)
An easy use wordcloud generating library
In this example I store generated wordcloud data to a parquet file
import pandas as pd
from wordcloud import WordCloud as wc
allText = 'some text....'
wd = wc().generate(allText)
words = []
for word in wd.layout_:
words.append(word[0])
df = pd.DataFrame(words)
A leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources
This example is how I use nltk to generate sentiment data
from nltk.sentiment.vader import SentimentIntensityAnalyzer
find = some Dataframe
sid = SentimentIntensityAnalyzer()
for title in find['title']:
ss = sid.polarity_scores(title)
if ss['neg'] > ss['pos']:
neg += 1
elif ss['neg'] != ss['pos']:
pos += 1
if ss['neu'] > 0.6:
neu += 1
A modern, fast (high-performance), web framework for building APIs
I use FastApi to provide data for my website demo
A powerful web app framework which shows my work beautifully
