Build a Tourism-Specific Sentiment Lexicon Via Word2vec

Wei Li, Luyao Zhu, Kun Guo (), Yong Shi () and Yuanchun Zheng
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Wei Li: University of Chinese Academy of Sciences
Luyao Zhu: University of Chinese Academy of Sciences
Kun Guo: University of Chinese Academy of Sciences
Yong Shi: Chinese Academy of Sciences
Yuanchun Zheng: University of Chinese Academy of Sciences

Annals of Data Science, 2018, vol. 5, issue 1, No 1, 7 pages

Abstract: 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.

Keywords: Sentiment analysis; Sentiment lexicon; Optimization; Tourism reviews (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s40745-017-0130-3

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