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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Marine-Roig E (2017) Online travel reviews: a massive paratextual analysis. Analytics in smart tourism design. Springer
Kim K, Park O, Yun S, Yun H (2017) What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management. Technol Forecast Soc Change 123:362–369
Olmedilla M, Martinez-Torres MR, Toral SL (2016) Examining the power-law distribution among eWOM communities: a characterisation approach of the Long Tail. Technol Anal Strateg Manag 28(5):601–613
González-Rodríguez M (2016) Post-visit and pre-visit tourist destination image through eWOM sentiment analysis and perceived helpfulness. Int J Contemp Hosp Manag 28:2609–2627
Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113
Das SR, Chen MY (2007) Yahoo! for Amazon: sentiment extraction from small talk on the web. Manag Sci 53(9):1375–1388
Strapparava C, Valitutti A (2004) WordNet-Affect: an affective extension of WordNet. In: Proceedings of the 4th international conference on language resources and evaluation. pp 1083–1086
Richardson SD, Dolan WB, Vanderwende L (1998) MindNet: acquiring and structuring semantic information from text. In: COLING-ACL’98: meeting of the association for computational linguistics. pp 1098–1102
Wu F, Huang Y, Song Y, Liu S (2015) Towards building a high-quality microblog-specific Chinese sentiment lexicon. Decis Support Syst 87:39–49
Xianghua F, Guo L, Yanyan G, Zhiqiang W (2013) Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowl Based Syst 37:186–195
Yang X et al (2016) Automatic construction and global optimization of a multi-sentiment lexicon. Comput Intell Neurosci 2016:2093406. https://doi.org/10.1155/2016/2093406
Miller GA, Beckwith R, Fellbaum C, Gross D, Miller KJ (1990) Introduction to wordnet: an on-line lexical database. Int J Lexicogr 3(4):235–244
Ku L, Liang Y, Chen H, Lun-Wei K, Yu-Ting L, Hsin-Hsi C (2006) Opinion extraction, summarization and tracking in news and blog corpora. Artif Intell 2001:100–107
Mikolov T, Corrado G, Chen K, Dean J (2013) Efficient estimation of word representations in vector space. In: Proceedings of the international conference on learning representations (ICLR 2013). pp 1–12
Wächter A, Biegler LT (2006) On the implementation of a primal-dual interior point filter line search algorithm for large-scale nonlinear programming. Math Program 106:25–57
Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends\(^{\textregistered }\) Inf Retr 2(1):459–526.
Rao D, Ravichandran D (2009) Semi-supervised polarity lexicon induction. In: EACL ‘09 Proceedings of the 12th conference of the European chapter of the association for computational linguistics, April. pp 675–682
Velikovich L, Kerry SB, Ryan H (2010) The viability of web-derived polarity lexicons. In: Naacl, June. pp 777–785
Acknowledgements
This research is supported by the National Natural Science Foundation of China Nos. 91546201, 71331005 and 71501175, Shandong Independent Innovation and Achievement Transformation Special Fund of China (2014ZZCX03302), and the Open Project of Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences.
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Li, W., Zhu, L., Guo, K. et al. Build a Tourism-Specific Sentiment Lexicon Via Word2vec. Ann. Data. Sci. 5, 1–7 (2018). https://doi.org/10.1007/s40745-017-0130-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40745-017-0130-3