Automated Sentiment Analysis in Tourism: Comparison of Approaches

被引:129
|
作者
Kirilenko, Andrei P. [1 ]
Stepchenkova, Svetlana O. [1 ]
Kim, Hany [2 ]
Li, Xiang [3 ]
机构
[1] Univ Florida, Coll Hlth & Human Performance, Dept Tourism Recreat & Sport Management, 240B Florida Gym,POB 118208, Gainesville, FL 32611 USA
[2] Mt St Vincent Univ, Dept Business & Tourism, Halifax, NS, Canada
[3] Temple Univ, Dept Tourism & Hosp Management, Philadelphia, PA 19122 USA
关键词
sentiment analysis; automated classification; social networks; surveys; recommender system; USER-GENERATED CONTENT; CLASSIFICATION; REVIEWS; EXPERIENCES; LEXICON;
D O I
10.1177/0047287517729757
中图分类号
F [经济];
学科分类号
02 ;
摘要
Interest in applying Big Data to tourism is increasing, and automated sentiment analysis has been used to extract public opinion from various sources. This article evaluates the suitability of different types of automated classifiers for applications typical in tourism, hospitality, and marketing studies by comparing their performance to that of human raters. While the commonly used performance indices suggest that on easier-to-classify data sets machine learning methods demonstrate performance comparable to that by human raters, other performance measures such as Cohen's kappa show that the results of machine learning are still inferior to manual processing. On more difficult and noisy data sets, automated analysis has poorer performance than human raters. The article discusses issues pertinent to selection of appropriate sentiment analysis software and offers a word of caution against using automated classifiers uncritically.
引用
收藏
页码:1012 / 1025
页数:14
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