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An enhanced sentiment dictionary for domain adaptation with multi-domain dataset in Tamil language (ESD-DA)

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Abstract

Mostly sentiment analysis employs dictionary approaches for recognizing the polarity of terms in a review. However, in sentiment analysis between different domains called domain adaptation (DA), the sentiment lexicon disappoints that leads to the feature mismatch problem. Now, many e-commerce sites try to process reviews in their native languages. In this paper, we propose an enhanced dictionary in our native language (Tamil) that aims at building contextual relationships among the terms of multi-domain datasets that tries to minimize the feature mismatch problem. The proposed dictionary employs both labeled and unlabeled data from the source domain and unlabeled data from the target domain. More precisely, the initial dictionary explores pointwise mutual information for calculating contextual weight then the final dictionary estimates the rank score based on the importance of terms among all the reviews. This work intends to classify reviews of multiple target domains in Tamil by using the unified dictionary with a large number of vocabularies. This extendible dictionary significantly improves the accuracy of DA with the other baseline methods and handles many words in multiple domains with ease.

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Notes

  1. https://www.britannica.com/topic/Tamil-language.

  2. https://en.wikipedia.org/wiki/List_of_languages_by_total_number_of_speakers.

  3. http://www.cs.jhu.edu/~mdredze/code.php.

  4. https://www.indiaglitz.com/tamil-movie-reviews.

  5. http://www.cs.jhu.edu/~mdredze/code.php.

  6. http://en.wikipedia.org/wiki/Listofemoticons.

  7. https://translate.google.com/toolkit.

  8. https://github.com/AshokR/TamilNLP/wiki/POS-Tagger.

  9. https://github.com/AshokR/~/TamilStopWords.txt.

  10. https://github.com/krishna41999/TamilSentimentDictionary.

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Acknowledgements

The authors thank the anonymous reviewers and our friends for their critics and suggestions.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tiruchirappalli, 600 015, India

    E. Sivasankar & P. Balasubramanian

  2. Department of Computer Science and Engineering, A.V.C. College of Engineering Mannampandal, Mayiladuthurai, 609 305, India

    K. Krishnakumari

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  1. E. Sivasankar
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  2. K. Krishnakumari
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  3. P. Balasubramanian
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Correspondence to K. Krishnakumari.

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Sivasankar, E., Krishnakumari, K. & Balasubramanian, P. An enhanced sentiment dictionary for domain adaptation with multi-domain dataset in Tamil language (ESD-DA). Soft Comput 25, 3697–3711 (2021). https://doi.org/10.1007/s00500-020-05400-x

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