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Deep Learning Using Context Vectors to Identify Implicit Aspects

IEEE access, 2023-01, Vol.11, p.1-1 [Peer Reviewed Journal]

ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2023.3268243 ;CODEN: IAECCG

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  • Title:
    Deep Learning Using Context Vectors to Identify Implicit Aspects
  • Author: Le Thi, Thuy ; Tran, Thien Khai ; Phan, Tuoi Thi
  • Subjects: Context awareness ; Context vector ; Deep learning ; dependency grammar ; Grammar ; implicit aspect ; Ontologies ; Sentiment analysis ; sentiment ontology ; Task analysis ; Urban areas
  • Is Part Of: IEEE access, 2023-01, Vol.11, p.1-1
  • Description: Aspects extraction is the key task in the sentiment analysis problem, which includes extraction of both explicit and implicit aspects. Identifying implicit aspects is not a new task in sentiment analysis, but it still presents many challenges. Numerous studies have addressed the issue and offered different approaches, but there are still fundamental challenges as follows: solving the context word problem; the domain-specific problem; and the implicit aspects represented by sentiment words that do not appear explicitly in the text. This paper proposes a method for identifying implicit aspects of sentiment words. To address these above challenges, the system is built on the foundation of deep learning with context vectors, dependency grammar, anaphora coreference resolution, and sentiment ontology. The dependency parser and the entity coreference resolver are adopted to filter out the sentiment-aspect pairs data generated from embedding context words. Then, a fine-tuned technique is applied, and the classifier corresponding to each sentiment word is built to identify implicit aspects. This combination has produced positive results, reaching almost 90% accuracy.
  • Publisher: IEEE
  • Language: English
  • Identifier: ISSN: 2169-3536
    EISSN: 2169-3536
    DOI: 10.1109/ACCESS.2023.3268243
    CODEN: IAECCG
  • Source: Directory of Open Access Journals
    IEEE Xplore Open Access Journals
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