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

Author

Listed:
  • Qin Li

    (Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu 610041, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Shaobo Li

    (School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
    Guizhou Provincial Key Laboratory of Public Big Data, 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
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Jianjun Hu

    (School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
    Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA)

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.

Suggested Citation

  • Qin Li & Shaobo Li & Jie Hu & Sen Zhang & Jianjun Hu, 2018. "Tourism Review Sentiment Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism and Topic-Enriched Word Vectors," Sustainability, MDPI, vol. 10(9), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:3313-:d:170239
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    References listed on IDEAS

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    1. Gang Ren & Taeho Hong, 2017. "Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach," Sustainability, MDPI, vol. 9(10), pages 1-18, September.
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    Cited by:

    1. Seungju Nam & Hyun Cheol Lee, 2019. "A Text Analytics-Based Importance Performance Analysis and Its Application to Airline Service," Sustainability, MDPI, vol. 11(21), pages 1-24, November.
    2. Guan, Keqin & Gong, Xu, 2023. "A new hybrid deep learning model for monthly oil prices forecasting," Energy Economics, Elsevier, vol. 128(C).
    3. Sergei Mikhailov & Alexey Kashevnik, 2020. "Tourist Behaviour Analysis Based on Digital Pattern of Life—An Approach and Case Study," Future Internet, MDPI, vol. 12(10), pages 1-16, September.
    4. Yuguo Tao & Feng Zhang & Chunyun Shi & Yun Chen, 2019. "Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions," Sustainability, MDPI, vol. 11(18), pages 1-23, September.

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