Sentiment Analysis of Typhoon Related Tweets using Standard and Bidirectional Recurrent Neural Networks

TL;DR

This study applies standard and bidirectional Recurrent Neural Networks to analyze sentiment in nearly 40,000 tweets related to Typhoon Yolanda in the Philippines, achieving high accuracy in classifying emotional content.

Contribution

It introduces a comparative analysis of standard and bidirectional RNNs for sentiment classification of disaster-related tweets in a Filipino context.

Findings

Bidirectional RNN achieved 87.69% accuracy in binary sentiment classification.

Majority of tweets expressed positive sentiments supporting victims.

Negative tweets accounted for 19.8%, highlighting sadness and despair.

Abstract

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.