Sentiment Analysis on Bangla and Romanized Bangla Text (BRBT) using Deep Recurrent models

TL;DR

This paper introduces a new dataset for sentiment analysis of Bangla and Romanized Bangla texts and evaluates deep recurrent models, specifically LSTM, on this dataset with promising results.

Contribution

The paper provides a substantial, validated dataset for Bangla and Romanized Bangla sentiment analysis and explores deep recurrent models with different loss functions and pre-training strategies.

Findings

LSTM models achieved promising sentiment classification accuracy.

Pre-training on one dataset improved results on the other.

Binary and categorical crossentropy loss functions yielded comparable performance.

Abstract

Sentiment Analysis (SA) is an action research area in the digital age. With rapid and constant growth of online social media sites and services, and the increasing amount of textual data such as - statuses, comments, reviews etc. available in them, application of automatic SA is on the rise. However, most of the research works on SA in natural language processing (NLP) are based on English language. Despite being the sixth most widely spoken language in the world, Bangla still does not have a large and standard dataset. Because of this, recent research works in Bangla have failed to produce results that can be both comparable to works done by others and reusable as stepping stones for future researchers to progress in this field. Therefore, we first tried to provide a textual dataset - that includes not just Bangla, but Romanized Bangla texts as well, is substantial, post-processed and multiple validated, ready to be used in SA experiments. We tested this dataset in Deep Recurrent model, specifically, Long Short Term Memory (LSTM), using two types of loss functions - binary crossentropy and categorical crossentropy, and also did some experimental pre-training by using data from one validation to pre-train the other and vice versa. Lastly, we documented the results along with some analysis on them, which were promising.