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It also promotes the development of various smart devices and social platforms. More and more people are now using smart devices to post their reviews right after something happens. In order to keep pace with this trend, we propose a method to analyze users\u2019 sentiment by using their text data. When analyzing users\u2019 text data, it is noted that a user\u2019s review may contain many aspects. Traditional text classification methods used by smart devices, however, usually ignore the importance of multiple aspects of a review. Additionally, most algorithms usually ignore the network structure information between the words in a sentence and the sentence itself. To address these issues, we propose a novel dual\u2010level attention\u2010based heterogeneous graph convolutional network for aspect\u2010based sentiment classification which minds more context information through information propagation along with graphs. Particularly, we first propose a flexible HIN (heterogeneous information network) framework to model the user\u2010generated reviews. This framework can integrate various types of additional information and capture their relationships to alleviate semantic sparsity of some labeled data. This framework can also leverage the full advantage of the hidden network structure information through information propagation along with graphs. Then, we propose a dual\u2010level attention\u2010based heterogeneous graph convolutional network (DAHGCN), which includes node\u2010level and type\u2010level attentions. The attention mechanisms can analyze the importance of different adjacent nodes and the importance of different types of nodes for the current node. The experimental results on three real\u2010world datasets demonstrated the effectiveness and reliability of our model.<\/jats:p>","DOI":"10.1155\/2021\/6625899","type":"journal-article","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T19:43:05Z","timestamp":1622144585000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Dual\u2010Level Attention Based on a Heterogeneous Graph Convolution Network for Aspect\u2010Based Sentiment Classification"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4436-811X","authenticated-orcid":false,"given":"Peng","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5654-7748","authenticated-orcid":false,"given":"Lei","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0722-152X","authenticated-orcid":false,"given":"Jianxun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3310-8347","authenticated-orcid":false,"given":"Dong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8699-8825","authenticated-orcid":false,"given":"Pei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1211-6329","authenticated-orcid":false,"given":"Yang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,5,27]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3061419"},{"key":"e_1_2_9_2_2","volume-title":"Reviews, Reputation, and Revenue: The Case of Yelp.com","author":"Luca M.","year":"2016"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.4018\/IJABE.2020100101"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/715730"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/5093620"},{"key":"e_1_2_9_6_2","unstructured":"BakshiR. 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