An Analysis on Machine Learning Approaches for Sentiment Analysis

被引：0
|
作者：
Shrivash, Brajesh Kumar [1 ]
Verma, Dinesh Kumar [1 ]
Pandey, Prateek [1 ]
机构：
[1] Jaypee Univ Engn & Technol, A-B Rd, Guna, MP, India
来源：
SMART SYSTEMS: INNOVATIONS IN COMPUTING (SSIC 2021) | 2022年 / 235卷
关键词：
D O I：
10.1007/978-981-16-2877-1_46
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Social media is a wide source of sharing user's opinions on different areas. These opinions are known as sentiments. Social media is an application of research for sentiment analysis. Sentiment research gives a direction to the organization about user's views on their products and services. Many approaches exist for sentiment analysis, and machine learning is one of them. This paper has selected research articles from the year 2013-2019 and studies these to find out the key insights on the most efficient ML techniques used in sentiment analysis. The analysis of the study concludes that the SVM and NB approaches of machine learning are more operationally efficient as compared to others.
引用
收藏
页码：499 / 513
页数：15
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