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- W4294831689 abstract "AbstractAspect-Based Sentiment Analysis (ABSA) task can analyze the sentiment of social reviews on different aspects. On the basis of ABSA, Targeted Aspect-Based Sentiment Analysis (TABSA) task additionally takes into account target entities. However, current studies on these tasks usually represent aspects based on word-level vectors, as static aspects and may not strongly correlated with review semantics, thus the effectiveness of sentiment prediction may be affected. Based on pre-trained BERT, we propose a sentiment analysis model using dynamic aspect representation. Specifically, we supplement external knowledge for limited review information, and integrate dynamic review features into static aspect. We also build a hierarchical interactive attention to fully explore the relationship among target, context and aspect. Several experiments are conducted on SentiHood dataset (for TABSA) and SemEval-2014 dataset (for ABSA). Our model has the best results in aspect detection and sentiment classification subtasks. Especially in contrast with BERT-pair, the accuracy of our model improves 1.2(%) and 0.6(%) in two subtasks on SentiHood dataset, and 1.9(%) and 1.4(%) on SemEval dataset.KeywordsTABSADynamic aspect representationHierarchical interactive attention" @default.
- W4294831689 created "2022-09-07" @default.
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- W4294831689 date "2022-01-01" @default.
- W4294831689 modified "2023-09-29" @default.
- W4294831689 title "Targeted Aspect-Based Sentiment Analysis by Utilizing Dynamic Aspect Representation" @default.
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- W4294831689 doi "https://doi.org/10.1007/978-3-031-15919-0_54" @default.
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