A Comparison of Multinomial Naïve Bayes and Bidirectional LSTM for Emotion Detection

Emotion detection is an area of sentiment analysis that focuses on the extraction and evaluation of feelings. Many deep learning and machine learning researchers have found emotional content in text. People’s lives all across the world were significantly impacted by the COVID-19 pandemic. The social networking site twitter were very helpful in documenting people’s emotion and views. In literature there are two methods that has been used extensively for the emotion identification; one is Multinomial Naive Bayes model, and another is Bidirectional Long Short-Term Memory (LSTM). In this research, we have identified the efficient approach among the two mentioned approaches. We have compared the multinomial naive bayes model and bidirectional LSTM for identifying emotion. To identify the emotion in text, the tweet from twitter is utilized; that contains emotions in the form of text. The results show that bidirectional Long-Short Term Memory approach outperforms as compared to the multinomial naïve bayes approach (MNB). The bidirectional LSTM approach enhances the emotion detection over the MNB approach. The time taken for the training of the tweets differs for the two approaches. Due to its longer training period, the multinomial naive bayes technique may not be suitable for use with huge datasets.