Tourism Review Sentiment Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism and Topic-Enriched Word Vectors

Qin Li, Shaobo Li, Jie Hu, Sen Zhang and Jianjun Hu
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Qin Li: Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu 610041, China
Shaobo Li: School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
Jie Hu: School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
Sen Zhang: Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu 610041, China
Jianjun Hu: School of Mechanical Engineering, Guizhou University, Guiyang 550025, China

Sustainability, 2018, vol. 10, issue 9, 1-15

Abstract: Sentiment analysis of online tourist reviews is playing an increasingly important role in tourism. Accurately capturing the attitudes of tourists regarding different aspects of the scenic sites or the overall polarity of their online reviews is key to tourism analysis and application. However, the performances of current document sentiment analysis methods are not satisfactory as they either neglect the topics of the document or do not consider that not all words contribute equally to the meaning of the text. In this work, we propose a bidirectional gated recurrent unit neural network model (BiGRULA) for sentiment analysis by combining a topic model (lda2vec) and an attention mechanism. Lda2vec is used to discover all the main topics of review corpus, which are then used to enrich the word vector representation of words with context. The attention mechanism is used to learn to attribute different weights of the words to the overall meaning of the text. Experiments over 20 NewsGroup and IMDB datasets demonstrate the effectiveness of our model. Furthermore, we applied our model to hotel review data analysis, which allows us to get more coherent topics from these reviews and achieve good performance in sentiment classification.

Keywords: topic model; attention mechanism; lda2vec; BiGRU; sentiment classification; tourism review (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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