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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.03259 |
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| _version_ | 1866912018526633984 |
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| author | Li, Chen Tang, Huidong Zhang, Jinli Guo, Xiujing Cheng, Debo Morimoto, Yasuhiko |
| author_facet | Li, Chen Tang, Huidong Zhang, Jinli Guo, Xiujing Cheng, Debo Morimoto, Yasuhiko |
| contents | Aspect-based sentiment analysis predicts sentiment polarity with fine granularity. While graph convolutional networks (GCNs) are widely utilized for sentimental feature extraction, their naive application for syntactic feature extraction can compromise information preservation. This study introduces an innovative edge-enhanced GCN, named SentiSys, to navigate the syntactic graph while preserving intact feature information, leading to enhanced performance. Specifically,we first integrate a bidirectional long short-term memory (Bi-LSTM) network and a self-attention-based transformer. This combination facilitates effective text encoding, preventing the loss of information and predicting long dependency text. A bidirectional GCN (Bi-GCN) with message passing is then employed to encode relationships between entities. Additionally, unnecessary information is filtered out using an aspect-specific masking technique. To validate the effectiveness of our proposed model, we conduct extensive evaluation experiments on four benchmark datasets. The experimental results demonstrate enhanced performance in aspect-based sentiment analysis with the use of SentiSys. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_03259 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing Aspect-Based Sentiment Analysis through Deep Learning Models Li, Chen Tang, Huidong Zhang, Jinli Guo, Xiujing Cheng, Debo Morimoto, Yasuhiko Computation and Language Artificial Intelligence Aspect-based sentiment analysis predicts sentiment polarity with fine granularity. While graph convolutional networks (GCNs) are widely utilized for sentimental feature extraction, their naive application for syntactic feature extraction can compromise information preservation. This study introduces an innovative edge-enhanced GCN, named SentiSys, to navigate the syntactic graph while preserving intact feature information, leading to enhanced performance. Specifically,we first integrate a bidirectional long short-term memory (Bi-LSTM) network and a self-attention-based transformer. This combination facilitates effective text encoding, preventing the loss of information and predicting long dependency text. A bidirectional GCN (Bi-GCN) with message passing is then employed to encode relationships between entities. Additionally, unnecessary information is filtered out using an aspect-specific masking technique. To validate the effectiveness of our proposed model, we conduct extensive evaluation experiments on four benchmark datasets. The experimental results demonstrate enhanced performance in aspect-based sentiment analysis with the use of SentiSys. |
| title | Advancing Aspect-Based Sentiment Analysis through Deep Learning Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2404.03259 |