A SURVEY: SENTIMENT ANALYSIS WITH DATA CLASSIFICATION USING MACHINE LEARNING ALGORITHMS

Abstract

The goal of sentiment analysis is to automatically determine the tone of a piece of written content. Monitoring social media, analyzing product evaluations, and evaluating consumer feedback are just a few of the numerous applications where it is growing increasingly. Machine learning algorithms have greatly enhanced the performance and accuracy of sentiment analysis. This paper provides an extensive overview of machine learning techniques for document and sentence-level aspect sentiment analysis. The shortcomings of more conventional machine learning methods for sentiment analysis are outlined. After that, numerous machine learning architectures that have been effectively used for this purpose was investigated. Furthermore, the difficulties of handling various types of data, including visual as well as multimodal data, along with how both methods have been modified to overcome these obstacles was considered. In addition, the ways sentiment analysis may be used in various fields, such as product evaluations and social media was investigated. Lastly, possible future research areas and draw attention to the present limits of machine learning algorithms for sentiment analysis was discussed. This survey is designed to give academics and practitioners a thorough grasp of the latest machine learning algorithms used for sentiment analysis while explaining how they work in practice.