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

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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.

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Notes

  1. www.internetslang.com.

  2. http://sentiment.christopherpotts.net.

  3. www.parallelpython.com.

  4. https://github.com/ravikiranj/twitter-sentiment-analyzer.

  5. http://ixa2.si.ehu.es/ukb/.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Govt. College of Engineering Kannur, Kannur, India

    Rafeeque Pandarachalil

  2. Department of Information Science & Technology, Anna University, Chennai, India

    Selvaraju Sendhilkumar

  3. Department of Computer Science & Engineering, Anna University, Chennai, India

    G. S. Mahalakshmi

Authors
  1. Rafeeque Pandarachalil
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  2. Selvaraju Sendhilkumar
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  3. G. S. Mahalakshmi
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Correspondence to Rafeeque Pandarachalil.

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Pandarachalil, R., Sendhilkumar, S. & Mahalakshmi, G.S. Twitter Sentiment Analysis for Large-Scale Data: An Unsupervised Approach. Cogn Comput 7, 254–262 (2015). https://doi.org/10.1007/s12559-014-9310-z

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