Summary of Advancing Aspect-based Sentiment Analysis Through Deep Learning Models, by Chen Li et al.
Advancing Aspect-Based Sentiment Analysis through Deep Learning Models
by Chen Li, Huidong Tang, Jinli Zhang, Xiujing Guo, Debo Cheng, Yasuhiko Morimoto
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Aspect-based sentiment analysis aims to predict sentiment polarity with fine granularity. Graph Convolutional Networks (GCNs) are widely used for sentimental feature extraction, but their naive application for syntactic feature extraction can compromise information preservation. This study introduces the edge-enhanced GCN, SentiSys, which navigates the syntactic graph while preserving intact feature information, leading to enhanced performance. The proposed model combines a Bi-LSTM network and a self-attention-based transformer for effective text encoding, preventing information loss and predicting long dependency text. A bidirectional GCN (Bi-GCN) with message passing is then employed to encode relationships between entities. Additionally, an aspect-specific masking technique filters out unnecessary information. Extensive evaluation experiments on four benchmark datasets validate the effectiveness of SentiSys in aspect-based sentiment analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study improves how computers understand people’s emotions by analyzing text at a more detailed level. Currently, some computer models don’t preserve important details when understanding sentences. The new model, called SentiSys, does a better job by using graph convolutional networks to analyze the relationships between words in a sentence. It also combines other techniques to make sure it doesn’t lose any important information. To test how well this works, the researchers used four different datasets and found that their new model performed better than previous models. |
Keywords
* Artificial intelligence * Feature extraction * Gcn * Lstm * Self attention * Transformer
