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SentiVerb system: classification of social media text using sentiment analysis

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Abstract

This article proposes novel frameworks of SentiVerb and Spell Checker system, which extracts the reaction, mood, and opinion of users from social media text (SMT). The opinion of users is extracted from their written text on social media such as comments, tweets, blogs, feedbacks etc. and are classified as positive or negative opinion based on sentiment score of SMT using dictionary-based approach and a binary classifier. The dictionary-based approach uses opinion verb dictionary (OVD) to extract the sentiment of opinion verbs present in SMT. This OVD contain only opinion verbs along with their sentiment score. The various steps of the framework such as lower-case conversion, tokenization, spell checker, Part-of-Speech tagging, stop word elimination, stemming, sentiment score calculation, and classification of SMT has been discussed. A new concept of threshold negative parameter is first time introduced in this article. In the experiment, the proposed SentiVerb system’s performance is evaluated on three datasets such as Facebook comments on goods and services tax (GST) implementation in India, tweets on the debate between former president of USA Mr. Barack Obama and Mr. John McCain, and the movie reviews. Consequently, the implementation of the proposed SentiVerb system using rule-based classifier (RBC) gives the best performance result in term of accuracy with 82.5% on GST comments and 79.18% on Obama-McCain debate, which is better than the existing algorithms on the social issues related domain dataset(s). Also, system performance (accuracy of 71.3%) is better than others results on standard movie dataset.

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Notes

  1. https://www.nltk.org/book/ch05.html

  2. Negation words are those words which reverse the polarity of the word if they come before the positive or negative opinion word within a sentence.

  3. https://developers.facebook.com/docs/reference/api/

  4. https://findmyfbid.com/

  5. https://json-csv.com/

  6. https://bitbucket.org/speriosu/updown/src/5de483437466/data/

  7. http://www.cs.cornell.edu/people/pabo/movie-review-data/

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Acknowledgements

The authors would like to thank Sant Longowal Institute of Engineering and Technology, Punjab, India for providing the systems and infrastructure to support this research work. Also thank to Dr. Sanjeev Prakash and Smt. Preetpal Kaur Buttar for proof reading.

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  1. Department of Computer Science & Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Sangrur, Punjab, 148106, India

    Shailendra Kumar Singh & Manoj Kumar Sachan

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Appendices

Appendix 1

Table 13 Abbreviation words list of social media text
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Appendix 2

Table 14 List of stop words for sentiment analysis
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Singh, S.K., Sachan, M.K. SentiVerb system: classification of social media text using sentiment analysis. Multimed Tools Appl 78, 32109–32136 (2019). https://doi.org/10.1007/s11042-019-07995-2

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