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Sentiment lexicons and non-English languages: a survey

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Abstract

The ever-increasing number of Internet users and online services, such as Amazon, Twitter and Facebook has rapidly motivated people to not just transact using the Internet but to also voice their opinions about products, services, policies, etc. Sentiment analysis is a field of study to extract and analyze public views and opinions. However, current research within this field mainly focuses on building systems and resources using the English language. The primary objective of this study is to examine existing research in building sentiment lexicon systems and to classify the methods with respect to non-English datasets. Additionally, the study also reviewed the tools used to build sentiment lexicons for non-English languages, ranging from those using machine translation to graph-based methods. Shortcomings are highlighted with the approaches along with recommendations to improve the performance of each approach and areas for further study and research.

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Notes

  1. http://stardict.sourceforge.net.

  2. http://www.dicts.info/uddl.php.

  3. translate.google.com.

  4. http://www.talkingcock.com/html/lexec.php.

  5. http://www.singlishdictionary.com/.

  6. https://en.wikipedia.org/wiki/Singlish_vocabulary.

  7. https://github.com/jeffreybreen/twitter-sentiment-analysis-tutorial-201107/tree/master/data/opinion-lexicon-English.

  8. https://www.mturk.com/.

  9. http://www.jeuxdemots.org/emot.php.

  10. https://github.com/facebookresearch/fastText.

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  1. Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Mohammed Kaity & Vimala Balakrishnan

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Kaity, M., Balakrishnan, V. Sentiment lexicons and non-English languages: a survey. Knowl Inf Syst 62, 4445–4480 (2020). https://doi.org/10.1007/s10115-020-01497-6

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