Indian Journal of Science and Technology

Abstract

Objectives: This study aims two main goals; one is to provide complete notions relevant to sentiment analysis by SA mechanisms, its categorization, and its techniques. The second goal is to make a comprehensive study of supervised learning techniques used in SA classification to summarize the different works conducted in this area and track the recent developments.

Methods: To achieve the first goal, several important survey studies, including modern and relevant works presented would be analyzed for full concepts around SA. As for the second objective of the study, the most important reports would be investigated, analyzed, and compared in the use of supervised learning techniques in SA from the previous to the recent researches till 2019.

Findings: This study also made a comprehensive research of the supervised machine learning classifiers used in SA, its recent techniques and enhancement methods and the suggestion future works. There are still some open challenges in this area such as mining the complex reviews and implicit aspect identification. The sentiment language is also a challenge; thus, addressing each language according to its attributes is a difficult task and so the sentiment domain issue.

Application/improvements: The information provided is used in assessing opinions and analyzing sentiment that could be used by researchers and institutions, and to identify different trends besides recommending the future research directions.

Keywords: Sentiment Analysis (SA), Opinion Mining (OM), Machine Learning, Supervised Learning, Sentiment Classification.