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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>To achieve outstanding aspect-level sentiment analysis (ASC), it is crucial to reduce the distance between aspect terms and opinion words. Recently, advanced methods in ASC used graph neural network (GNN)-based methods to leverage the syntactic dependency within the sentence, which can shorten the distance through syntactical dependencies. However, existing approaches that utilize GNNs have difficulty extracting long-distance relations in the dependency tree due to the over-smoothing problem resulting from stacking GNN layers, which limits their ability to detect remote relations. To solve this issue, we propose a Bidirectional Directed Acyclic Graph (BDAG) to reconstruct syntactic dependencies and a Bidirectional Directed Acyclic Graph Neural Network (BDAGNN) to efficiently propagate multi-hop sentiment information. We also enhance the BDAG with affective commonsense knowledge from SenticNet for comprehensive sentiment classification. The BDAGNN we proposed obtains partial state-of-the-art performance on four benchmark datasets, indicating the feasibility of encoding syntactic structures with BDAG.<\/jats:p>","DOI":"10.1145\/3716501","type":"journal-article","created":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T09:09:43Z","timestamp":1739005783000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Bidirectional Directed Acyclic Graph Neural Network for Aspect-level Sentiment Classification"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9276-9367","authenticated-orcid":false,"given":"Junjie","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer Science, East China Normal University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7229-2741","authenticated-orcid":false,"given":"Luwei","family":"Xiao","sequence":"additional","affiliation":[{"name":"East China Normal University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2843-898X","authenticated-orcid":false,"given":"Anran","family":"Wu","sequence":"additional","affiliation":[{"name":"East China Normal University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4079-7897","authenticated-orcid":false,"given":"Tianlong","family":"Ma","sequence":"additional","affiliation":[{"name":"East China Normal University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7712-4734","authenticated-orcid":false,"given":"Daoguo","family":"Dong","sequence":"additional","affiliation":[{"name":"East China Normal University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4723-5486","authenticated-orcid":false,"given":"Liang","family":"He","sequence":"additional","affiliation":[{"name":"East China Normal University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,3,23]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1","volume-title":"The AAAI Conference on Artificial Intelligence","volume":"2","author":"Cambria E.","year":"2014","unstructured":"E. 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