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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2409.14000 |
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| _version_ | 1866914954464985088 |
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| author | Wu, Linxiao Luo, Yuanshuai Zhu, Binrong Liu, Guiran Wang, Rui Yu, Qian |
| author_facet | Wu, Linxiao Luo, Yuanshuai Zhu, Binrong Liu, Guiran Wang, Rui Yu, Qian |
| contents | Amidst the swift evolution of social media platforms and e-commerce ecosystems, the domain of opinion mining has surged as a pivotal area of exploration within natural language processing. A specialized segment within this field focuses on extracting nuanced evaluations tied to particular elements within textual contexts. This research advances a composite framework that amalgamates the positional cues of topical descriptors. The proposed system converts syntactic structures into a matrix format, leveraging convolutions and attention mechanisms within a graph to distill salient characteristics. Incorporating the positional relevance of descriptors relative to lexical items enhances the sequential integrity of the input. Trials have substantiated that this integrated graph-centric scheme markedly elevates the efficacy of evaluative categorization, showcasing preeminence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14000 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature Wu, Linxiao Luo, Yuanshuai Zhu, Binrong Liu, Guiran Wang, Rui Yu, Qian Computation and Language Artificial Intelligence Amidst the swift evolution of social media platforms and e-commerce ecosystems, the domain of opinion mining has surged as a pivotal area of exploration within natural language processing. A specialized segment within this field focuses on extracting nuanced evaluations tied to particular elements within textual contexts. This research advances a composite framework that amalgamates the positional cues of topical descriptors. The proposed system converts syntactic structures into a matrix format, leveraging convolutions and attention mechanisms within a graph to distill salient characteristics. Incorporating the positional relevance of descriptors relative to lexical items enhances the sequential integrity of the input. Trials have substantiated that this integrated graph-centric scheme markedly elevates the efficacy of evaluative categorization, showcasing preeminence. |
| title | Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2409.14000 |