Publication

Abstract : With the evolving digital era, the amount of online data generated such as product reviews in different languages via various social media platforms. Information analysis is very beneficial for many companies such as online service providers. This task of interpreting and classifying the emotions behind the text (review) using text analysing techniques is known as sentiment analysis (SA). Sometimes, the sentence might have positive as well as negative polarity at the same time, giving rise to conflict situations where the SA models might not be able to predict the polarity precisely. This problem can be solved using aspect-based sentiment analysis (ABSA) that identifies fine-grained opinion polarity towards a specific aspect associated with a given target. The aspect category helps us to understand the sentiment analysis problem better. ABSA on the Hindi benchmark dataset, having reviews from multiple web sources, is performed in this work. The proposed model has used two different word embedding algorithms, namely Word2Vec and fastText for feature generation and various machine learning (ML) and deep learning (DL) models for classification. For the ABSA task, the LSTM model outperformed other ML and DL models with 57.93 and 52.32% accuracy, using features from Word2Vec and fastText, respectively. Mostly, the performance of classification models with Word2Vec embedding was better than the models with fastText embedding.