Twitter Sentiment Analysis for Large-Scale Data: An Unsupervised Approach

被引：67

作者：

Pandarachalil, Rafeeque ^{[1
]}

Sendhilkumar, Selvaraju ^{[2
]}

Mahalakshmi, G. S. ^{[3
]}

机构：

[1] Govt Coll Engn Kannur, Dept Comp Sci & Engn, Kannur, India

[2] Anna Univ, Dept Informat Sci & Technol, Chennai 600025, Tamil Nadu, India

[3] Anna Univ, Dept Comp Sci & Engn, Chennai 600025, Tamil Nadu, India

来源：

COGNITIVE COMPUTATION | 2015年 / 7卷 / 02期

关键词：

Sentiment analysis; Twitter; SentiWordNet; SenticNet; Parallel [!text type='python']python[!/text; NETWORKS;

D O I：

10.1007/s12559-014-9310-z

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Millions of tweets are generated each day on multifarious issues. Topical diversity in content demands domain-independent solutions for analysing twitter sentiments. Scalability is another issue when dealing with huge amount of tweets. This paper presents an unsupervised method for analysing tweet sentiments. Polarity of tweets is evaluated by using three sentiment lexicons-SenticNet, SentiWordNet and SentislangNet. SentislangNet is a sentiment lexicon built from SenticNet and SentiWordNet for slangs and acronyms. Experimental results show fairly good -score. The method is implemented and tested in parallel python framework and is shown to scale well with large volume of data on multiple cores.

引用

页码：254 / 262

页数：9

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