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

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 s...

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Published in:	Cognitive computation Vol. 7; no. 2; pp. 254 - 262
Main Authors:	Pandarachalil, Rafeeque, Sendhilkumar, Selvaraju, Mahalakshmi, G. S.
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01-04-2015 Springer Nature B.V
Subjects:	Abbreviations Accuracy Algorithms Artificial Intelligence Biomedical and Life Sciences Biomedicine Computation by Abstract Devices Computational Biology/Bioinformatics Data mining Dictionaries Frequency distribution Machine learning Natural language Neurosciences Product reviews Semantics Sentiment analysis Slang Social networks Sentiment analysis Twitter Parallel python SentiWordNet SenticNet
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Abstract	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 F -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.
AbstractList	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 F-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. 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 F -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.
Author	Mahalakshmi, G. S. Pandarachalil, Rafeeque Sendhilkumar, Selvaraju
Author_xml	– sequence: 1 givenname: Rafeeque surname: Pandarachalil fullname: Pandarachalil, Rafeeque email: rafeeqpc@gcek.ac.in organization: Department of Computer Science and Engineering, Govt. College of Engineering Kannur – sequence: 2 givenname: Selvaraju surname: Sendhilkumar fullname: Sendhilkumar, Selvaraju organization: Department of Information Science & Technology, Anna University – sequence: 3 givenname: G. S. surname: Mahalakshmi fullname: Mahalakshmi, G. S. organization: Department of Computer Science & Engineering, Anna University
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SubjectTerms	Abbreviations Accuracy Algorithms Artificial Intelligence Biomedical and Life Sciences Biomedicine Computation by Abstract Devices Computational Biology/Bioinformatics Data mining Dictionaries Frequency distribution Machine learning Natural language Neurosciences Product reviews Semantics Sentiment analysis Slang Social networks
Title	Twitter Sentiment Analysis for Large-Scale Data: An Unsupervised Approach
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