Sentiment Analysis of Bangla Language Using Deep Learning Approaches

Muntasir Hoq, Promila Haque, Mohammed Nazim Uddin

Abstract: Emotion is the most important gear for human textual communication with each other via social media. Nowadays, people use text for reviewing or recommending things, sharing opinions, rating their choices or unlikeness, providing feedback for different services, and so on. Bangladeshi people use Bangla to express their emotions. Current research based on sentiment analysis has got low-performance output by using several approaches on detecting sentiment polarity and emotion from Bangla texts. In this study, we have developed four models with the hybrid of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) with various Word Embeddings including Embedding Layer, Word2Vec, Global Vectors (Glove), and Continuous Bag of Words (CBOW) to detect emotion from Bangla texts (words, sentences). Our models can define the basic three emotions; happiness, anger, and sadness. It will make interaction lively and interesting. Our comparisons are bestowed against CNN, LSTM with different Word Embeddings, and also against some previous researches with the same dataset based on classical Machine Learning techniques such as Support Vector Machine (SVM), Naïve Bayes, and K-Nearest Neighbors (K-NN). In our proposed study, we have used Facebook Bangla comments for a suitable dataset. In our study, we have tried to detect the exact emotion from the text. And in result, the best model integrating Word2Vec embedding layer with a hybrid of CNN-LSTM detected emotions from raw textual data with an accuracy of 90.49% and F1 score of 92.83%.