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Build a Tourism-Specific Sentiment Lexicon Via Word2vec

Author

Listed:
  • Wei Li

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Luyao Zhu

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Kun Guo

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Yong Shi

    (Chinese Academy of Sciences
    Chinese Academy of Sciences
    University of Nebraska at Omaha)

  • Yuanchun Zheng

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

Abstract

Online travel and online travel culture developed fast in China recently years while useful knowledge still hidden under a large number of tourism reviews. Therefore, we need effective sentiment analysis methods to mine useful knowledge which can help tourism websites make decisions and improve their travel products. Some data-driven sentiment lexicons have poor performance on sentiment polarity classification due to lack of semantic information. Thus, we propose an effective and more proper data-driven sentiment lexicon construction method incorporating manually labeled sentiment scores, semantic similarity information that is introduced by machine learning method word2vec. Experimental results demonstrate that our method improves the performance of tourism sentiment analysis significantly.

Suggested Citation

  • Wei Li & Luyao Zhu & Kun Guo & Yong Shi & Yuanchun Zheng, 2018. "Build a Tourism-Specific Sentiment Lexicon Via Word2vec," Annals of Data Science, Springer, vol. 5(1), pages 1-7, March.
    • Handle: RePEc:spr:aodasc:v:5:y:2018:i:1:d:10.1007_s40745-017-0130-3
    DOI: 10.1007/s40745-017-0130-3
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    References listed on IDEAS

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    1. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    2. Kim, Kun & Park, Oun-joung & Yun, Seunghyun & Yun, Haejung, 2017. "What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management," Technological Forecasting and Social Change, Elsevier, vol. 123(C), pages 362-369.
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