Sentiment Analysis Based Product Rating Using Textual Reviews

被引：0

作者：

Sindhu, C. ^{[1
]}

Vyas, Dyawanapally Veda ^{[1
]}

Pradyoth, Kommareddy ^{[1
]}

机构：

[1] SRM Univ, Dept Comp Sci & Engn, Chennai, Tamil Nadu, India

来源：

2017 INTERNATIONAL CONFERENCE OF ELECTRONICS, COMMUNICATION AND AEROSPACE TECHNOLOGY (ICECA), VOL 2 | 2017年

关键词：

Sentiment analysis; opinion mining; Rapid Miner; SVM; Naive Bayes; SELECTION;

D O I：

暂无

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

With the rapid growth of web technology there is a huge amount of data present in the web for internet users. Such data is mainly from the social media such as Facebook 141, twitter, etc., where millions of people express their views in their daily interaction which can be their sentiments or opinions about a particular thing. Large amount of data also present in the forms of reviews and ratings in many online shopping websites such as Amazon, Flip cart, snap deal etc., In order to automate the analysis of such data the area of Sentiment analysis is used. Before performing sentiment analysis the data is subjected to many pre-processing techniques and then identifying opinion data in the reviews and classifying them according to their polarity confidence i.e., whether they fall under positive or negative or neutral connotation. The open source data tool analysis tool called rapid miner is used to perform the step by step explanation of review processing. This paper also presents a comparative study of algorithms like SVM and Naive Bayes.

引用

页码：727 / 731

页数：5

共 20 条

[1] Text feature selection using ant colony optimization [J].

Aghdam, Mehdi Hosseinzadeh ;

Ghasem-Aghaee, Nasser ;

Basiri, Mohammad Ehsan .

EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (03) :6843-6853

[2]

Ang J.L., APPL TEXT MINING UND

[3]

[Anonymous], 2005, P ACL 05 43ND M ASS

[4]

Bekkerman Ron., 2011, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, P231, DOI DOI 10.1145/2020408.2020449

[5] Selection of relevant features and examples in machine learning [J].

Blum, AL ;

Langley, P .

ARTIFICIAL INTELLIGENCE, 1997, 97 (1-2) :245-271

[6]

Bond M., 2009, P 23 BRIT HCI GROUP

[7] A survey on feature selection methods [J].

Chandrashekar, Girish ;

Sahin, Ferat .

COMPUTERS & ELECTRICAL ENGINEERING, 2014, 40 (01) :16-28

[8]

claypo Niphat, OPINION MINING THAI

[9]

Dhanalakshmi V., 2016, OPINION MINING STUDE

[10]

ElTazi Khaled Ahmed Neamat, SENTIMENT ANAL SOCIA

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