Word- and Sentence-Level Representations for Implicit Aspect Extraction

Bibliographic Data

ID22108809
AuthorsPantelis Agathangelou (0000-0002-9897-4879, University of Nicosia), Ioannis Katakis (0000-0001-5283-9965, University of Nicosia), Panagiotis Kasnesis (0000-0003-3607-8187, University of West Attica)
Year2024
Volume11
Issue5
Pages5935-5948
Publication date2024-10-01
Open AccessYes
TypeARTICLE
VenueIEEE Transactions on Computational Social Systems (JOURNAL)
Journal identifiersISSN: 2329-924X • E-ISSN: 2373-7476
PublisherInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/tcss.2024.3391833
OpenAlexW4396886561
LanguageEN
Citations received1
References cited39

Aspect terms extraction (ATE), a key subtask for aspect-based sentiment analysis, opinion summarization, and topic modeling aims at extracting grammatical elements (nouns, phrases, and adjectives) from user reviews that reveal the discussed features of the entity under review. These aspect terms are usually the targets of the opinions expressed. Identifying them requires tackling substantial linguistic challenges but, due to the multiple commercial and social applications, significant research effort has been invested in efficiently mining aspects. Recent advances in ATE address methods that exploit a sentence or a word-level encoding of a user review as a solution. This article proposes a novel and effective word- and sentence-level encoding framework, which utilizes a neural network architecture that learns to extract aspect terms. The main advantage of our approach is that it can extract explicit and implicit aspects (i.e., aspects that are not directly mentioned in the user-generated text). We evaluate our method on four widely used datasets where we prove its efficiency against state-of-the-art alternative approaches

Linguistics · Natural language processing · Sentence · Speech recognition · Advanced Text Analysis Techniques · Computer Science · Philosophy · Artificial Intelligence

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