Article: Multimodal sentiment analysis of tourism evaluation based on attention mechanism and neural network Journal: International Journal of Computational Intelligence Studies (IJCISTUDIES) 2024 Vol.13 No.1/2 pp.95 - 111 Abstract: Travel review text reviews objectively reflect travellers' real perceptions of tourist destinations and services, and are also one of the important ways of internet word-of-mouth communication. According to the study, travellers will obtain information about products and other travellers' reviews of tourism through various channels before making purchase decisions, and use them as the basis for whether to continue purchasing. With the help of the research method of big data, the text takes the travel virtual community and the online review text of tourism on the web platform as the research material. To address the problems of previous models, we propose an attention-based mechanism LSTM, called SA-BiLSTM, for travel evaluation sentiment analysis. We integrate the attention mechanism into the LSTM and use it to improve the representation capability of the LSTM. The attention mechanism ignores the distance between words, which effectively solves the gradient disappearance and gradient explosion problems encountered by LSTM, and the combination of LSTM and attention mechanism is equivalent to model fusion at the structural level, which enables the model to capture information in different directions in the text and enhances the robustness of the model. We validate the good results of our model relative to state-of-the-art models on numerous real datasets. Inderscience Publishers - linking academia, business and industry through research

Title: Multimodal sentiment analysis of tourism evaluation based on attention mechanism and neural network

Authors: Mei Zhao

Addresses: School of Economics and Management, Xinjiang Institute of Engineering, Urumqi, Xinjiang, 830000, China

Abstract: Travel review text reviews objectively reflect travellers' real perceptions of tourist destinations and services, and are also one of the important ways of internet word-of-mouth communication. According to the study, travellers will obtain information about products and other travellers' reviews of tourism through various channels before making purchase decisions, and use them as the basis for whether to continue purchasing. With the help of the research method of big data, the text takes the travel virtual community and the online review text of tourism on the web platform as the research material. To address the problems of previous models, we propose an attention-based mechanism LSTM, called SA-BiLSTM, for travel evaluation sentiment analysis. We integrate the attention mechanism into the LSTM and use it to improve the representation capability of the LSTM. The attention mechanism ignores the distance between words, which effectively solves the gradient disappearance and gradient explosion problems encountered by LSTM, and the combination of LSTM and attention mechanism is equivalent to model fusion at the structural level, which enables the model to capture information in different directions in the text and enhances the robustness of the model. We validate the good results of our model relative to state-of-the-art models on numerous real datasets.

Keywords: tourism evaluation analysis; attention mechanism; recurrent neural network; RNN; sentiment analysis.

DOI: 10.1504/IJCISTUDIES.2024.144048

International Journal of Computational Intelligence Studies, 2024 Vol.13 No.1/2, pp.95 - 111

Received: 11 Apr 2023
Accepted: 29 Aug 2023
Published online: 22 Jan 2025 *

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Title: Multimodal sentiment analysis of tourism evaluation based on attention mechanism and neural network

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