A Comprehensive Survey on Aspect Based Sentiment Analysis

TL;DR

This survey comprehensively reviews various methodologies in Aspect Based Sentiment Analysis, highlighting their differences and providing a comparative analysis to advance understanding in this NLP sub-field.

Contribution

It offers an in-depth comparison of ABSA methodologies, filling a gap by systematically analyzing and categorizing existing approaches.

Findings

Different ABSA approaches vary in accuracy and complexity

Supervised methods generally outperform unsupervised ones

The survey highlights promising future research directions

Abstract

Aspect Based Sentiment Analysis (ABSA) is the sub-field of Natural Language Processing that deals with essentially splitting our data into aspects ad finally extracting the sentiment information. ABSA is known to provide more information about the context than general sentiment analysis. In this study, our aim is to explore the various methodologies practiced while performing ABSA, and providing a comparative study. This survey paper discusses various solutions in-depth and gives a comparison between them. And is conveniently divided into sections to get a holistic view on the process.