Aspect-Based Sentiment Analysis: A Comprehensive Review and Open Research Challenges

The social web provides a facility for common people to share their views, comments, feedback, and experiences on various social media platforms. Due to these platforms now communication has become easier and it has provided us with opportunities to use social media channels for various businesses. The survival of an e-commerce business highly relies on customers’ opinions or feedback extensively articulated on internet-based social media platforms or social networking sites. Eventually, analysis of these public opinions from these platforms to identify and demonstrate the cumulative meaningful information is the prime objective of Sentiment Analysis (SA). Summarization of this informative knowledge is advantageous for companies, organizations, and industrialist analysts to improve the quality of their products or services. In this scenario, Aspect-Based Sentiment Analysis (ABSA) has proven to be a powerful companion for companies, organizations, and producers to specify the consumers’ attitudes and opinions towards products and brands’ impressive features. Various efforts have contributed to aspect extraction and sentiment classification over the last few decades. In this review study, we first focus on two diverse research tasks, aspect extraction, and aspect sentiment analysis and then we present a comprehensive review of existing studies in various classifications such as lexicon-based, graph data, topic models, machine learning, and deep learning. This diverse analysis provides pros and cons for various research approaches and comparative analysis. We also discuss various sources and details of datasets, which are used in this research study. We also present the research gaps including open research challenges as a guide for future researchers in the form of future research. Moreover, we also present the bibliometric analysis of the aspect-based sentiment analysis.