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In this paper, BERT model was used to vectorize the review text of tourist attractions, and fusion attention mechanism and long and short-term memory model were used to extract the emotional features of the text for classification at the feature extraction layer. The emotional accuracy of the model proposed in this paper reached 95.79% in the review text of tourist attractions.<\/jats:p>","DOI":"10.3233\/jcm-247135","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T12:08:39Z","timestamp":1718712519000},"page":"1605-1615","source":"Crossref","is-referenced-by-count":6,"title":["Sentiment analysis of tourism review text combined with bert-bilstm and attention mechanism"],"prefix":"10.66113","volume":"24","author":[{"given":"Dengyun","family":"Zhu","sequence":"first","affiliation":[{"name":"Key Laboratory of China\u2019s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Jing","sequence":"additional","affiliation":[{"name":"Key Laboratory of China\u2019s Ethnic Languages and Intelligent Processing of Gansu Province, Northwest Minzu University, Lanzhou, Gansu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of China\u2019s Ethnic Languages and Intelligent Processing of Gansu Province, Northwest Minzu University, Lanzhou, Gansu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongjiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of China\u2019s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fucheng","family":"Wan","sequence":"additional","affiliation":[{"name":"Key Laboratory of China\u2019s Ethnic Languages and Intelligent Processing of Gansu Province, Northwest Minzu University, Lanzhou, Gansu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"4","key":"10.3233\/JCM-247135_ref1","doi-asserted-by":"crossref","first-page":"325","DOI":"10.14311\/NNW.2018.28.019","article-title":"A method of fine-grained short text sentiment analysis based on machine learning","volume":"28","author":"Chang","year":"2018","journal-title":"Neural Network World."},{"key":"10.3233\/JCM-247135_ref2","doi-asserted-by":"crossref","unstructured":"et al.Design of text sentiment analysis tool using feature extraction based on fusing machine learning algorithms, Journal of Intelligent & Fuzzy Systems.,2021;40 (4):6375\u20136383.","DOI":"10.3233\/JIFS-189478"},{"issue":"3","key":"10.3233\/JCM-247135_ref3","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1177\/0165551519837187","article-title":"An improved evidence-based aggregation method for sentiment analysis","volume":"46","author":"Khiabani","year":"2020","journal-title":"Journal of Information Science."},{"key":"10.3233\/JCM-247135_ref4","doi-asserted-by":"crossref","unstructured":"Mee A et al., Sentiment analysis using TF-IDF weighting of UK MPs\u2019 tweets on Brexit. 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