Sentiment Analysis of Bangla Text Using Gated Recurrent Neural Network

Abstract

Sentiment analysis is a fundamental part of Natural Language Processing. There are numerous works on this topic in English and other languages. However, it is still a comparatively new practice in Bangla. The absence of a suitable Bangla corpus is the primary obstacle for sentiment analysis tasks in Bangla. Nonetheless, Long Short-term Memory (LSTM) is a common technique for resolving sentiments from a dataset containing a large amount of text data. However, Gated Recurrent Unit (GRU) is very efficient for datasets with a low amount of text data. In this manuscript, we present a 5- layered GRU neural network model, each layer comprising of 48 neurons, applied the model on an existing Bangla corpus. We implemented the 10-folds cross-validation approach and repeated the same processes three times. Each time, we considered the averages of the ten validation accuracy and losses and compared the results with the state-of-the-art published outcome (77.85% highest accuracy) for Bi-directional LSTM (BLSTM). The highest accuracies for our model was 78.41%, while the lowest accuracy was 76.34%.