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Brazil-Elections-Twitter-Sentiment

Twitter Sentiment Analysis using sklearn

Description:

  • Program that collects data from Twitter API and classify it into sentiment categories based on positive and negative Amazon reviews

Model Type

Ensemble Model - Decision Tree Classifier, Logistic Regression, Random Forest, KNN

20/10/2022

Version - V1.3

Goals:

  • To build a ML model that is able to predict the sentiment from twitter hashtags, posts and profiles

Key Insights and Notes

  • Since this is an Election sentiment prediction, values can be affect everyday by new information
  • We are translating the tweets from Portuguese to English, this can affect the model's accuracy
  • The ensemble model was able to improve the accuracy by 5% comparing it to the previous model
  • An ensemble model is not necessarily better than any other model, you should be able to read the results and pick the best model
  • Repeated tweets might be affecting the performance, you can choose to remove it
  • Hyperparameters Optimization was not done yet

Fixes

  • Translation to Portuguese Added

Version Updates:

  • Ensemble Model added
  • API connections
  • Classify by hashtags
  • Sklearn implemented
  • Amazon reviews database
  • Save the model
  • Statistics
  • Added sentiment database
  • Comparisons added
  • Language translation added
  • Remove duplicates

Future Implementations:

  • Plot Analytical charts
  • Add start_time and end_time parameters
  • Create a list with market symbol and their sentiment
  • Add time period analysis(select period option)
  • Create sentiment dictionaries from web studies
  • Hyperparameters Optimization
  • Train and test with the Brazilian dataset
  • Analyze use of prepositions
  • Create a Market Checker were it indicates the sentiment on the period and Prints the Chart Bullish or Bearish

Author - Luiz Gabriel Bongiolo

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