Name
Abstract | ||
|---|---|---|
Sentiment analysis is widely applied in personalized recommendation, business reputation monitoring, and consumer-driven product design and quality improvement. Fine-grained sentiment analysis, aimed at directly predicting sentiment polarity for multiple pre-defined fine-grained categories in an end-to-end way without having to identify aspect words, is more flexible and effective for real world applications. Constructing high performance fine-grained sentiment analysis models requires the effective use of both shared document level features and category-specific features, which most existing multi-task models fail to accomplish. In this paper, we propose an effective multi-task neural network for fine-grained sentiment analysis, Multi-Task Multi-Head Attention Memory Network (MMAM). To make full use of the shared document level features and category-specific features, our framework adopts a multi-head document attention mechanism as the memory to encode shared document features, and a multi-task attention mechanism to extract category-specific features. Experiments on two Chinese language fine-grained sentiment analysis datasets in the Restaurant-domain and Automotive-domain demonstrate that our model consistently outperforms other compared fine-grained sentiment analysis models. We believe extracting and fully utilizing document level features to establish category-specific features is an effective approach to fine-grained sentiment analysis. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-32233-5_47 | Lecture Notes in Artificial Intelligence |
Keywords | DocType | Volume |
Fine-grained sentiment analysis,Multi-head Attention Memory,Multi-task learning | Conference | 11838 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zehui Dai | 1 | 0 | 0.34 |
| Wei Dai | 2 | 0 | 2.37 |
| Zhenhua Liu | 3 | 0 | 0.34 |
| Fengyun Rao | 4 | 0 | 0.34 |
| Huajie Chen | 5 | 0 | 0.68 |
| Guangpeng Zhang | 6 | 0 | 0.34 |
| Yadong Ding | 7 | 0 | 0.68 |
| Jiyang Liu | 8 | 0 | 0.34 |