Sentiment Analysis of Twitter Data

被引：15

作者：

Wang, Yili ^{[1
,2
,3
]}

Guo, Jiaxuan ^{[1
,2
]}

Yuan, Chengsheng ^{[1
,2
]}

Li, Baozhu ^{[4
]}

机构：

[1] Nanjing Univ Informat Sci & Technol, Engn Res Ctr Digital Forens, Minist Educ, Nanjing 210044, Peoples R China

[2] Nanjing Univ Informat Sci & Technol, Sch Comp Sci, Nanjing 210044, Peoples R China

[3] Sungkyunkwan Univ, Coll Informat & Commun Engn, Suwon 16419, South Korea

[4] Zhuhai Fudan Innovat Inst, Internet Things & Smart City Innovat Platform, Zhuhai 519031, Peoples R China

来源：

APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 22期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

sentiment analysis; text classification; natural language processing; Twitter; PARTICLE SWARM OPTIMIZATION; FEATURE-SELECTION; TEXT; CLASSIFICATION; ALGORITHMS; EMOTION;

D O I：

10.3390/app122211775

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Twitter has become a major social media platform and has attracted considerable interest among researchers in sentiment analysis. Research into Twitter Sentiment Analysis (TSA) is an active subfield of text mining. TSA refers to the use of computers to process the subjective nature of Twitter data, including its opinions and sentiments. In this research, a thorough review of the most recent developments in this area, and a wide range of newly proposed algorithms and applications are explored. Each publication is arranged into a category based on its significance to a particular type of TSA method. The purpose of this survey is to provide a concise, nearly comprehensive overview of TSA techniques and related fields. The primary contributions of the survey are the detailed classifications of numerous recent articles and the depiction of the current direction of research in the field of TSA.

引用

页数：14

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