Mining and Application of Tourism Online Review Text Based on Natural Language Processing and Text Classification Technology

Abstract

This paper firstly describes the research status of online review text mining and finds out the problems existing in the mining and application of tourism texts. Aiming at these problems, this paper proposes a text mining method for tourism online reviews based on natural language processing and text classification technology. The first step is to analyze the validity of the online review text; the purpose is to remove the invalid text and improve the mining efficiency of the online review text. The second step is to conduct a comprehensive evaluation of scenic spots and hotels based on text classification technology and sentiment analysis. The comprehensive evaluation indicators are established for the five core service contents. High-quality scenic spots and hotels are selected according to the ranking of comprehensive evaluation. The third step is to propose a mining method of tourism hot words based on natural language processing for the selected high-quality tourist locations. The obtained hot words can intuitively show the impression of tourists on the scenic spot. The fourth step is to use mutual information combined with the left and right entropy to discover new words and to mine service characteristics of high-quality scenic spots and hotel from the new words. Finally, the proposed new methods are tested on the crawled tourism online review texts. The experimental results show that the novel comprehensive evaluation method proposed in this paper can truly and objectively select high-quality scenic spots and hotels and provide an important basis for the decision-making of tourism management. On this basis, hot words and new words can be effectively excavated from relevant online review texts, and travel impressions can be fed back from various aspects and angles.

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“…Choosing the suitable model is crucial for improving efficiency, as the process of training deep learning models involves several complex steps. The first step is data preprocessing, in which raw data must be cleaned, normalized, and transformed into a suitable format for the model 27 , 28 . This is followed by the initialization of the model architecture, which determines the layers and parameters of the model 29 .…”

“…The F1 value is a composite metric used to measure the precision and recall of a classification model, and is the summed average of precision and recall. The evaluation metric is calculated as follows 28 : where TP , FP , TN , and FN denote the four outcomes of true positive, false positive, true negative, and false negative for the confusion matrix, respectively 36 , 37 . A true positive is an outcome correctly predicted as positive by the model.…”

“…Jieba is a word segmentation system designed specifically for Chinese. It can break down sentences into sequences of words with actual meaning 28 . After segmenting the text using Jieba, we further optimized it by using a stop word list specifically designed for Chinese.…”

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“…Language is the most reliable and common way for humans to make their inner thoughts known in a way that other people can understand [ 49 ]. Texts are the direct expressions of users’ needs and emotions, so text-based analysis in the tourism sector has the potential to transform the industry, since decision-making is undoubtedly directly influenced by the experiences of trips of other tourists, made in comments on social networks or through blogs [ 80 ]. These texts can provide valuable insights for potential tourists and assist them in optimally choosing a destination, as well as exploring travel routes, or they can also help entrepreneurs in the tourism sector to improve their value proposition [ 78 ].…”

“…It also allows creating transparency, segmentation of the population, replacement or support in the decision-making of individuals [ 79 ]. This is the importance of carrying out data analysis, since through comprehensive evaluation indicators, the analysis can be used to create an offer of personalized intelligent services for both tourists and tourism providers [ 80 ].…”