Cognitive Computation

, Volume 7, Issue 2, pp 254–262 | Cite as

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

  • Rafeeque PandarachalilEmail author
  • Selvaraju Sendhilkumar
  • G. S. Mahalakshmi


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.


Sentiment analysis Twitter SentiWordNet SenticNet Parallel python 


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Rafeeque Pandarachalil
    • 1
    Email author
  • Selvaraju Sendhilkumar
    • 2
  • G. S. Mahalakshmi
    • 3
  1. 1.Department of Computer Science and EngineeringGovt. College of Engineering KannurKannurIndia
  2. 2.Department of Information Science & TechnologyAnna UniversityChennaiIndia
  3. 3.Department of Computer Science & EngineeringAnna UniversityChennaiIndia

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