A Literature Review on Application of Sentiment Analysis Using Machine Learning Techniques

Abstract

Many businesses are using social media networks to deliver different services and connect with clients and collect information about the thoughts and views of individuals. Sentiment analysis is a technique of machine learning that senses polarities such as positive or negative thoughts within the text, full documents, paragraphs, lines, or subsections. Machine Learning (ML) is a multidisciplinary field, a mixture of statistics and computer science algorithms that are commonly used in predictive and classification analyses. This paper presents the common techniques of analyzing sentiment from a machine learning perspective. In light of this, this literature review explores and discusses the idea of Sentiment analysis by undertaking a systematic review and assessment of corporate and community white papers, scientific research articles, journals, and reports. The goal and primary objectives of this article are to analytically categorize and analyze the prevalent research techniques and implementations of Machine Learning techniques to Sentiment Analysis on various applications. The limitation of this analysis is that by excluding the hardware and the theoretical exposure pertinent to the subject, the main emphasis is on the application side alone. The limitation of this study is that the major focus is on the application side thereby excluding the hardware and theoretical aspects related to the subject. Finally, this paper includes a research proposal for e-commerce environment towards sentiment analysis applying machine learning algorithms.