SentiEntityRec: Entity-level sentiment perception graph neural network for news recommendation
Abstract Precise news recommendations are critical in today’s digital landscape. However, conventional approaches overlook fine-grained sentiment nuances associated with individual entities in news content. This paper presents SentiEntityRec, a novel graph neural network framework that enriches trad...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-06-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44443-025-00087-2 |
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| _version_ | 1849332210564333568 |
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| author | Qingshuai Wang Jiahao Wang Noor Farizah Ibrahim |
| author_facet | Qingshuai Wang Jiahao Wang Noor Farizah Ibrahim |
| author_sort | Qingshuai Wang |
| collection | DOAJ |
| description | Abstract Precise news recommendations are critical in today’s digital landscape. However, conventional approaches overlook fine-grained sentiment nuances associated with individual entities in news content. This paper presents SentiEntityRec, a novel graph neural network framework that enriches traditional entity embeddings using a global graph-enhanced model by incorporating sentiment vectors. Experiments conducted with MIND datasets have shown that SentiEntityRec surpasses existing models in key performance metrics. For example, the proposed model improves +0.5% AUC over GLoCIM. These results underscore the superior efficacy of incorporating entity-sentiment analysis into graph-based news recommendation systems. |
| format | Article |
| id | doaj-art-4cd525c881464f9b835be5d292d32d2b |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-4cd525c881464f9b835be5d292d32d2b2025-08-20T03:46:16ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-06-0137511910.1007/s44443-025-00087-2SentiEntityRec: Entity-level sentiment perception graph neural network for news recommendationQingshuai Wang0Jiahao Wang1Noor Farizah Ibrahim2School of Computer Sciences, Universiti Sains MalaysiaSchool of Computer Sciences, Universiti Sains MalaysiaSchool of Computer Sciences, Universiti Sains MalaysiaAbstract Precise news recommendations are critical in today’s digital landscape. However, conventional approaches overlook fine-grained sentiment nuances associated with individual entities in news content. This paper presents SentiEntityRec, a novel graph neural network framework that enriches traditional entity embeddings using a global graph-enhanced model by incorporating sentiment vectors. Experiments conducted with MIND datasets have shown that SentiEntityRec surpasses existing models in key performance metrics. For example, the proposed model improves +0.5% AUC over GLoCIM. These results underscore the superior efficacy of incorporating entity-sentiment analysis into graph-based news recommendation systems.https://doi.org/10.1007/s44443-025-00087-2Sentiment AnalysisGraph Neural NetworksNews RecommendationPersonalization |
| spellingShingle | Qingshuai Wang Jiahao Wang Noor Farizah Ibrahim SentiEntityRec: Entity-level sentiment perception graph neural network for news recommendation Journal of King Saud University: Computer and Information Sciences Sentiment Analysis Graph Neural Networks News Recommendation Personalization |
| title | SentiEntityRec: Entity-level sentiment perception graph neural network for news recommendation |
| title_full | SentiEntityRec: Entity-level sentiment perception graph neural network for news recommendation |
| title_fullStr | SentiEntityRec: Entity-level sentiment perception graph neural network for news recommendation |
| title_full_unstemmed | SentiEntityRec: Entity-level sentiment perception graph neural network for news recommendation |
| title_short | SentiEntityRec: Entity-level sentiment perception graph neural network for news recommendation |
| title_sort | sentientityrec entity level sentiment perception graph neural network for news recommendation |
| topic | Sentiment Analysis Graph Neural Networks News Recommendation Personalization |
| url | https://doi.org/10.1007/s44443-025-00087-2 |
| work_keys_str_mv | AT qingshuaiwang sentientityrecentitylevelsentimentperceptiongraphneuralnetworkfornewsrecommendation AT jiahaowang sentientityrecentitylevelsentimentperceptiongraphneuralnetworkfornewsrecommendation AT noorfarizahibrahim sentientityrecentitylevelsentimentperceptiongraphneuralnetworkfornewsrecommendation |