Aspect-Based Sentiment Analysis of Bangla Comments on Entertainment Domain

Abstract

Low-resource natural language processing is getting more attention nowadays. Aspect-Based Sentiment Analysis (ABSA) from a high-resource language such as English becomes unchallenging because of sufficient datasets and experimentation tools. However, Aspect-Based Sentiment Analysis from low-resource languages such as Bangla is quite hard. So, many researchers are investing their time and knowledge in low-resource natural language processing. In this paper, we are proposing a Bangla Aspect-Based Sentiment Analysis model using Bangla natural language processing. We have collected 4012 Bangla text comments related to cricket, drama, movie, and music from YouTube. We have applied some very prominent supervised machine learning techniques such as Support Vector Classifier (SVC), Random Forest (RF), and Linear Regression (LR). We have achieved more than 75% accuracy in classifying positive, negative, and neutral sentiments and 80% accuracy in extracting aspects from Bangla texts. Finally, we used publicly available datasets to test our proposed model's generalizability. Furthermore, we find that our proposed approach surpasses earlier related research.