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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.10048 |
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| _version_ | 1866915203855155200 |
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| author | Sharma, Kavita Patel, Ritu Iyer, Sunita |
| author_facet | Sharma, Kavita Patel, Ritu Iyer, Sunita |
| contents | In this paper, we propose a novel method to enhance sentiment analysis by addressing the challenge of context-specific word meanings. It combines the advantages of a BERT model with a knowledge graph based synonym data. This synergy leverages a dynamic attention mechanism to develop a knowledge-driven state vector. For classifying sentiments linked to specific aspects, the approach constructs a memory bank integrating positional data. The data are then analyzed using a DCGRU to pinpoint sentiment characteristics related to specific aspect terms. Experiments on three widely used datasets demonstrate the superior performance of our method in sentiment classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_10048 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Knowledge Graph Enhanced Aspect-Level Sentiment Analysis Sharma, Kavita Patel, Ritu Iyer, Sunita Computation and Language In this paper, we propose a novel method to enhance sentiment analysis by addressing the challenge of context-specific word meanings. It combines the advantages of a BERT model with a knowledge graph based synonym data. This synergy leverages a dynamic attention mechanism to develop a knowledge-driven state vector. For classifying sentiments linked to specific aspects, the approach constructs a memory bank integrating positional data. The data are then analyzed using a DCGRU to pinpoint sentiment characteristics related to specific aspect terms. Experiments on three widely used datasets demonstrate the superior performance of our method in sentiment classification. |
| title | Knowledge Graph Enhanced Aspect-Level Sentiment Analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2312.10048 |