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The early sentiment analysis methods were mainly text\u2010level and sentence\u2010level, which believed that a text had only one sentiment. This phenomenon will cover up the details, and it is difficult to reflect people\u2019s fine\u2010grained and comprehensive sentiments fully, leading to people\u2019s wrong decisions. Obviously, aspect\u2010level sentiment analysis can obtain a more comprehensive sentiment classification by mining the sentiment tendencies of different aspects in the comment text. However, the existing aspect\u2010level sentiment analysis methods mainly focus on attention mechanism and recurrent neural network. They lack emotional sensitivity to the position of aspect words and tend to ignore long\u2010term dependencies. In order to solve this problem, on the basis of Bidirectional Encoder Representations from Transformers (BERT), this paper proposes an effective aspect\u2010level sentiment analysis approach (ALM\u2010BERT) by constructing an aspect feature location model. Specifically, we use the pretrained BERT model first to mine more aspect\u2010level auxiliary information from the comment context. Secondly, for the sake of learning the expression features of aspect words and the interactive information of aspect words\u2019 context, we construct an aspect\u2010based sentiment feature extraction method. Finally, we construct evaluation experiments on three benchmark datasets. The experimental results show that the aspect\u2010level sentiment analysis performance of the ALM\u2010BERT approach proposed in this paper is significantly better than other comparison methods.<\/jats:p>","DOI":"10.1155\/2021\/5534615","type":"journal-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T03:46:22Z","timestamp":1628653582000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Aspect\u2010Level Sentiment Analysis Approach via BERT and Aspect Feature Location Model"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8412-2547","authenticated-orcid":false,"given":"Guangyao","family":"Pang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keda","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoying","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyi","family":"Mo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0716-9998","authenticated-orcid":false,"given":"Zizhen","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoxing","family":"Pu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,8,10]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113234"},{"key":"e_1_2_10_2_2","doi-asserted-by":"crossref","unstructured":"CaiZ.andHeZ. 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