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Research on Sentiment Analysis of Scenic Area Comments Based on RoBerta-BiGRU-Attention—Taking Shenyang as an Example

DOI: 10.12677/HJDM.2023.134031, PP. 312-326

Keywords: RoBerta词向量,BiGRU,注意力机制,情感分析,潜在狄利克雷分布
RoBerta Word Vector
, BiGRU, Attention Mechanism, Sentiment Analysis, Latent Dirichlet Allocation

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The opinions and feelings expressed by tourists in the comments can intuitively reflect their evaluation of the tourist destination, and at the same time, the tone is clear and the emotion is strong. The resulting huge and dynamic information space needs to be understood and navigated jointly by consumers and product/service providers. Aiming at the problem of low accuracy of emotion classification in online comment texts of scenic spots, an improved model based on RoBerta word vectors and bidirectional gated recurrent units (BiGRU) is proposed. The Roberta model, which can represent the rich semantic features of texts, is used to represent word vectors. The BiGRU neural network, which can retain text context information for a long time, improves the classification effect of the model. On this basis, an attention mechanism is introduced to highlight the weight of emotional words in the text that can better express the classification results, and improve the accuracy of emotion classification. The above models were used to classify and predict emotional polarity on the comment data of three scenic spots in Shenyang City. The experimental results show that the model has achieved good performance on each data set. At the same time, combined with the LDA topic model analysis, the expectations and appeals of tourists’ comments are obtained, and technical support and future development opinions are provided for the development of Shen- yang’s tourism industry.


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