360 degree view of cross-domain opinion classification: a survey

Abstract

In the field of natural language processing and text mining, sentiment analysis (SA) has received huge attention from various researchers’ across the globe. By the prevalence of Web 2.0, user’s became more vigilant to share, promote and express themselves along with any issues or challenges that are being encountered on daily activities through the Internet (social media, micro-blogs, e-commerce, etc.) Expression and opinion are a complex sequence of acts that convey a huge volume of data that pose a challenge for computational researchers to decode. Over the period of time, researchers from various segments of public and private sectors are involved in the exploration of SA with an aim to understand the behavioral perspective of various stakeholders in society. Though the efforts to positively construct SA are successful, challenges still prevail for efficiency. This article presents an organized survey of SA (also known as opinion mining) along with methodologies or algorithms. The survey classifies SA into categories based on levels, tasks, and sub-task along with various techniques used for performing them. The survey explicitly focuses on different directions in which the research was explored in the area of cross-domain opinion classification. The article is concluded with an objective to present an exclusive and exhaustive analysis in the area of opinion mining containing approaches, datasets, languages, and applications used. The observations made are expected to support researches to get a greater understanding on emerging trends and state-of-the-art methods to be applied for future exploration.

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Notes

  1. 1.

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

  2. 2.

    http://nlp.stanford.edu/sentiment/.

  3. 3.

    http://www.cs.uic.edu/˜liub/FBS/sentiment-analysis.html.

  4. 4.

    https://code.google.com/p/word2vec/.

  5. 5.

    https://addons.mozilla.org/.

  6. 6.

    http://mallet.cs.umass.edu/.

  7. 7.

    http://www.ecmlpkdd2006.org/challenge.html.

  8. 8.

    http://people.csail.mit.edu/jrennie/20newsgroups.

  9. 9.

    http://ictclas.org/.

  10. 10.

    www.searchforum.org.cn/tansongbo/corpus/Dangdang_Book_4000.rar.

  11. 11.

    www.searchforum.org.cn/tansongbo/corpus/Ctrip_htl_4000.rar.

  12. 12.

    www.searchforum.org.cn/tansongbo/corpus/Jingdong_NB_4000.rar.

  13. 13.

    http://www.dangdang.com/.

  14. 14.

    http://www.ctrip.com/.

  15. 15.

    http://www.360buy.com/.

  16. 16.

    http://people.cs.umass.edu/~mccallum/data.html.

  17. 17.

    http://people.csail.mit.edu/jrennie/20Newsgroups.

  18. 18.

    http://www.daviddlewis.com/resources/testcollections.

  19. 19.

    http://www.cse.ust.hk/TL/dataset/Reuters.zip.

  20. 20.

    http://www.csie.ntu.edu.tw/cjlin/libsvm/.

  21. 21.

    http://word2vec.googlecode.com/svn/trunk/word2vec.c.

  22. 22.

    http://www.douban.com/.

  23. 23.

    http://www.datatang.com/data/44317.

  24. 24.

    http://www.datatang.com/data/12990.

  25. 25.

    http://www.chokkan.org/software/classias/.

  26. 26.

    http://sentiwordnet.isti.cnr.it/.

  27. 27.

    www.numpy.org.

  28. 28.

    http://scikit-learn.org/.

  29. 29.

    https://nlp.stanford.edu/software/tagger.shtml.

  30. 30.

    http://www.tripadvisor.com/.

  31. 31.

    http://www.csie.ntu.edu.tw/∼cjlin/libsvm/.

  32. 32.

    http://www.cs.jhu.edu/∼mdredze/datasets/sentiment/.

  33. 33.

    http://babelnet.org.

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Singh, R.K., Sachan, M.K. & Patel, R.B. 360 degree view of cross-domain opinion classification: a survey. Artif Intell Rev 54, 1385–1506 (2021). https://doi.org/10.1007/s10462-020-09884-9

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Keywords

  • Opinion mining
  • Sentiment analysis
  • Cross-domain opinion classification
  • Domain adaptation
  • Transfer learning
  • Machine learning