BAN-ABSA: An Aspect-Based Sentiment Analysis dataset for Bengali and it's baseline evaluation

TL;DR

This paper introduces BAN-ABSA, a high-quality, manually annotated Bengali dataset for aspect-based sentiment analysis, and provides baseline evaluations using deep learning models to facilitate future research in Bengali sentiment analysis.

Contribution

The paper presents the first high-quality Bengali dataset for aspect-based sentiment analysis along with baseline deep learning model evaluations.

Findings

CNN outperforms in accuracy for aspect extraction

Bi-LSTM outperforms CNN in F1-score for sentiment classification

Baseline accuracy achieved is 78.75% for aspect extraction and 71.08% for sentiment classification

Abstract

Due to the breathtaking growth of social media or newspaper user comments, online product reviews comments, sentiment analysis (SA) has captured substantial interest from the researchers. With the fast increase of domain, SA work aims not only to predict the sentiment of a sentence or document but also to give the necessary detail on different aspects of the sentence or document (i.e. aspect-based sentiment analysis). A considerable number of datasets for SA and aspect-based sentiment analysis (ABSA) have been made available for English and other well-known European languages. In this paper, we present a manually annotated Bengali dataset of high quality, BAN-ABSA, which is annotated with aspect and its associated sentiment by 3 native Bengali speakers. The dataset consists of 2,619 positive, 4,721 negative and 1,669 neutral data samples from 9,009 unique comments gathered from some famous Bengali news portals. In addition, we conducted a baseline evaluation with a focus on deep learning model, achieved an accuracy of 78.75% for aspect term extraction and accuracy of 71.08% for sentiment classification. Experiments on the BAN-ABSA dataset show that the CNN model is better in terms of accuracy though Bi-LSTM significantly outperforms CNN model in terms of average F1-score.