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Automatic construction of domain sentiment lexicon for semantic disambiguation

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Abstract

Sentiment lexicon is used to judge the sentiments of words and plays a significant role in sentiment analysis. Existing sentiment lexicons ignore the sentimental ambiguity of words in different contexts and only assign sentiment positive or negative polarity for words. In this paper, we propose an automatic method for the construction of the domain-specific sentiment lexicon (SDS-lex) to avoid sentimental ambiguity, which incorporates the sentiment information not only from the existing lexicons but also from the corpus by using our improved TF-IDF algorithm (ITF-IDF). The ITF-IDF algorithm calculates the sentiment of words by considering both the importance of words and the distribution of different part-of-speech (POS) in a corpus labeled with different sentiment tendencies. Experiments on real-world datasets show that our constructed lexicon improves the sentimental ambiguity and outperforms many existing lexicons in terms of the coverage and the accuracy when performing text sentiment classification tasks.

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Notes

  1. http://ai.stanford.edu/~amaas//data/sentiment/

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

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61801440), the High-quality and Cutting-edge Disciplines Construction Project for Universities in Beijing (Internet Information, Communication University of China) and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. Information Engineering Institute, Communication University of China, Beijing, 100024, China

    Yanyan Wang, Fulian Yin & Jianbo Liu

  2. Laboratory for Industrial and Applied Mathematics, York University, Toronto, M3J 1P3, Canada

    Marco Tosato

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  1. Yanyan Wang
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  2. Fulian Yin
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  4. Marco Tosato
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Correspondence to Fulian Yin.

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Wang, Y., Yin, F., Liu, J. et al. Automatic construction of domain sentiment lexicon for semantic disambiguation. Multimed Tools Appl 79, 22355–22373 (2020). https://doi.org/10.1007/s11042-020-09030-1

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