Comparison of Tourist Thematic Sentiment Analysis Methods Based on Weibo Data

LIU Siye, TIAN Yuan†, FENG Yuning, ZHUANG Yulong

ACTA Scientiarum Naturalium Universitatis Pekinensis - - Contents -

Institute of Remote Sensing and Geographical Information System, Peking University, Beijing 100871; † Corresponding author, E-mail:

Abstract Six tourism themes, diet, entertainment, shopping, view, transportation, and accommodation, are selected for thematic sentiment analysis. 53140 Weibo items published by Chinese tourists in Japan are collected and manually labeled as the case study dataset. Maximum Entropy model and Support Vector Machine are adopted. The training results are both fairly good, where the resulting Maximum Entropy model prevails slightly. It can be concluded that machine learning models are reasonably feasible in tourist thematic sentiment analysis. Moreover, the experiment also shows that the models can be improved by introducing emoticon icons and thematic words as supplements to traditional word features. Key words thematic sentiment analysis; Weibo of tourists; Maximum Entropy; Support Vector Machine (SVM)

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