Aspect-Based Sentiment Analysis using Machine Learning and Deep Learning Approaches

Abstract

Sentiment analysis (SA) is also known as opinion mining, it is the process of gathering and analyzing people's opinions about a particular service, good, or company on websites like Twitter, Facebook, Instagram, LinkedIn, and blogs, among other places. This article covers a thorough analysis of SA and its levels. This manuscript's main focus is on aspect-based SA, which helps manufacturing organizations make better decisions by examining consumers' viewpoints and opinions of their products. The many approaches and methods used in aspect-based sentiment analysis are covered in this review study (ABSA). The features associated with the aspects were manually drawn out in traditional methods, which made it a time-consuming and error-prone operation. Nevertheless, these restrictions may be overcome as artificial intelligence develops. Therefore, to increase the effectiveness of ABSA, researchers are increasingly using AI-based machine learning (ML) and deep learning (DL) techniques. Additionally, certain recently released ABSA approaches based on ML and DL are examined, contrasted, and based on this research, gaps in both methodologies are discovered. At the conclusion of this study, the difficulties that current ABSA models encounter are also emphasized, along with suggestions that can be made to improve the efficacy and precision of ABSA systems.

Deep Learning-Based Aspect Term Extraction for Sentiment Analysis in Hindi

“…The significant works done for aspect term extraction and aspect-based sentiment analysis in general and in the Hindi language are discussed in the literature survey (3)(4)(5)(6)(7)(8) . Md Shad Akhtar et al (9) reported the first supervised approach to the aspect term extraction task; they created an annotated dataset of high quality and built a machine learning model for sentiment analysis to show practical usage of the dataset.…”