2022
DOI: 10.1007/s00500-022-06793-7
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Self-Attention Networks and Adaptive Support Vector Machine for aspect-level sentiment classification

Abstract: Aspect-level sentiment classification aims to integrating the context to predict the sentiment polarity of aspect-specific in a text, which has been quite useful and popular, e.g. opinion survey and products' recommending in ecommerce. Many recent studies exploit a Long Short-Term Memory (LSTM) networks to perform aspect-level sentiment classification, but the limitation of long-term dependencies is not solved well, so that the semantic correlations between each two words of the text are ignored. In addition, … Show more

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Cited by 8 publications
(2 citation statements)
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“…Authors mine keywords and applied feature engineering techniques to explore patterns ( Asgarnezhad, Monadjemi & Aghaei, 2022 ). Aspect-level sentiment analysis has been performed using an adaptive SVM model and Twitter dataset ( Liu et al, 2022 ). Du et al (2022) applied a gated attention model for sentiment classification.…”
Section: Related Workmentioning
confidence: 99%
“…Authors mine keywords and applied feature engineering techniques to explore patterns ( Asgarnezhad, Monadjemi & Aghaei, 2022 ). Aspect-level sentiment analysis has been performed using an adaptive SVM model and Twitter dataset ( Liu et al, 2022 ). Du et al (2022) applied a gated attention model for sentiment classification.…”
Section: Related Workmentioning
confidence: 99%
“…ABSA mainly deals with two tasks including aspect extraction (AE) and aspect-based sentiment classi cation (ASC). Aspect extraction is to extract the aspect words in the text, while aspect-based sentiment classi cation is with the purpose of identifying the sentiment polarity of the given aspect in the text (Liu et al 2022;Zhang et al 2019). The polarity of a sentence is identi ed not only by context or opinion words, but also by considering aspect words.…”
Section: Introductionmentioning
confidence: 99%