Sentiment analysis of travel reviews based on deep learning and transformer

Yifan Wang

doi:10.1117/12.3027114

1 April 2024 Sentiment analysis of travel reviews based on deep learning and transformer

Yifan Wang

Proceedings Volume 13077, Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024); 1307707 (2024) https://doi.org/10.1117/12.3027114
Event: 4th International Conference on Signal Processing and Machine Learning (CONF-SPML 2024), 2024, Chicago, IL, United States

Abstract

The rapid expansion of the Internet and the utilization of big data have significantly contributed to a transformative shift in the tourism industry. As online travel reviews become more abundant, they provide insights into sentiments and attitudes related to travel experiences. This paper mainly concentrates on sentiment analysis of travel reviews utilizing deep learning methods and Transformer models. In particular, we explore the benefits of deep learning, specifically the Bi-LSTM, BERT, and ERNIE models. Rigorous comparative experiments on a database comprising 6,000 travel reviews from Henan Province, China are conducted. Experimental results demonstrate the advantage of the ERNIE model, which incorporates knowledge integration and diverse training tasks. The ERNIE model achieves a prominent enhancement in accuracy, recall and F1 score compared to the previous models. The findings underscore the efficacy of pre-trained language models in sentiment analysis tasks and their capacity to comprehend context and semantic nuances, leading to enhanced performance in sentiment classification.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yifan Wang "Sentiment analysis of travel reviews based on deep learning and transformer", Proc. SPIE 13077, Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024), 1307707 (1 April 2024); https://doi.org/10.1117/12.3027114

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