⛅ Weather Sentiments Predictor

Implemented a RNN model to predict the sentiments of weather tweets.

• Context + Inspiration: Sentiment analysis has became more popular in recent years especially in the political scene where comments are scraped online to give a preliminary forecast of the polling results. This project aim to expose us to NLP by forecasting the weather using twitter tweets

• The Long Short-term Memory (LSTM) model offered only a slightly better performance over Support Vector Machine (SVM) and a fully optimized Random Forest (RF) model. This could be improved if we used a Random Search approach for the regularization (dropout) Accuracy:
  • LSTM - 87.4%
  • SVM - 83.7%
  • Fully optimized RF - 85.6%

• Challenges: The algorithm has to be built in R which proved to be difficult as I was unfamiliar with developing a deep learning model in that language

• Technologies used: R