Research Article
Sentiment Analysis Approaches: A Systematic Review
@INPROCEEDINGS{10.4108/eai.16-4-2022.2318164, author={Pankaj Kumar Gautam and Subhadra Shaw}, title={Sentiment Analysis Approaches: A Systematic Review}, proceedings={Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India}, publisher={EAI}, proceedings_a={THEETAS}, year={2022}, month={6}, keywords={sentiment analysis lexicon-based method machine learning method hybrid method}, doi={10.4108/eai.16-4-2022.2318164} }- Pankaj Kumar Gautam
Subhadra Shaw
Year: 2022
Sentiment Analysis Approaches: A Systematic Review
THEETAS
EAI
DOI: 10.4108/eai.16-4-2022.2318164
Abstract
The number of consumers who leave online comments has risen dramatically as the number of global Internet presentations continues to rise. Ample data, if correctly explored, should yield important insights. More and more individuals are sharing their ideas and opinions online as a result of the fast rise of social media on the web, forum debates such as reviews, news, comments, and blogs. As a result, this intriguing issue is becoming increasingly significant in business and society. Sentiment analysis is a subcategory of natural language pro-cessing and is used to determine the polarity of the user's opinion, which may be positive, negative, or neutral, against the entities or their aspects. SA offers a plethora of current applications in a variety of industries. In the commercial world, it enables organizations to automatically collect client feedback on their products or services. It may be used in politics to infer popular sentiment and reaction to political events, which aids decision-making. This paper explores the various lexicon-based, machine learning (ML), and hybrid techniques used in the field of SA. Hybrid approaches have proved more efficient than any other individual approach.