Sentiment Analysis of Typhoon Related Tweets using Standard and Bidirectional Recurrent Neural Networks
This work addresses sentiment analysis for disaster response in a specific domain, but it is incremental as it applies standard methods to new data.
The study tackled sentiment analysis of tweets related to Typhoon Yolanda in the Philippines, achieving 81.79% accuracy for fine-grained classification with standard RNN and 87.69% for binary classification with bidirectional RNN, and found that 51.1% of tweets were positive, 19.8% negative, and 29% neutral.
The Philippines is a common ground to natural calamities like typhoons, floods, volcanic eruptions and earthquakes. With Twitter as one of the most used social media platform in the Philippines, a total of 39,867 preprocessed tweets were obtained given a time frame starting from November 1, 2013 to January 31, 2014. Sentiment analysis determines the underlying emotion given a series of words. The main purpose of this study is to identify the sentiments expressed in the tweets sent by the Filipino people before, during, and after Typhoon Yolanda using two variations of Recurrent Neural Networks; standard and bidirectional. The best generated models after training with various hyperparameters achieved a high accuracy of 81.79% for fine-grained classification using standard RNN and 87.69% for binary classification using bidirectional RNN. Findings revealed that 51.1% of the tweets sent were positive expressing support, love, and words of courage to the victims; 19.8% were negative stating sadness and despair for the loss of lives and hate for corrupt officials; while the other 29% were neutral tweets from local news stations, announcements of relief operations, donation drives, and observations by citizens.