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

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