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: Qingshuai Wang, Jiahao Wang, Noor Farizah Ibrahim
Format: Article
Language:English
Published: Springer 2025-06-01
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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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