Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis (2023.findings-acl)
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| Challenge: | Typical approaches do not exploit the potential of historical reviews or do not make full use of user/product associations. |
| Approach: | They propose to use historical reviews to initialize user and product representations and incorporate textual associations via a user-product cross-context module. |
| Outcome: | The proposed method outperforms existing state-of-the-art models on IMDb, Yelp and Longformer benchmarks. |
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| Challenge: | Existing work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review. |
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| Challenge: | Personality is a defining feature of human beings, shaped by a complex interplay of demographic characteristics, moral principles, and social experiences. |
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| Challenge: | Existing methods to predict sentiments on social media are limited and do not consider reciprocal influences among social media users. |
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Sentiment Analysis using the Relationship between Users and Products (2023.findings-acl)
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| Challenge: | Existing studies focus on modelling user and product aspects without considering the relationship between users and products. |
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