Emotion Recognition and Sentiment Classification using BERT with Data Augmentation and Emotion Lexicon Enrichment

Abstract

The emergence of social networking sites has paved the way for researchers to collect and analyze massive data volumes. Twitter, being one of the leading micro-blogging sites worldwide, provides an excellent opportunity for its users to express their states of mind via short text messages known as tweets. Much research focusing on identifying emotions and sentiments conveyed through tweets has been done. We propose a BERT model fine-tuned to the emotion recognition and sentiment classification tasks and show that it performs better than previous models on standard datasets. We also explore the effectiveness of data augmentation and data enrichment for these tasks.