TDAM: A topic-dependent attention model for sentiment analysis |
| |
| Institution: | 1. School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 611731, China;2. School of Business, Yunnan University of Finance and Economics, Kunming 650221, China;3. School of Business and Management, Shanghai International Studies University, Shanghai 201620, China;1. School of Data and Computer Science, Sun Yat-sen University, China;2. School of Computing Science, University of Glasgow, Glasgow, UK;3. School of Computer Science, The University of Adelaide, Adelaide, Australia;1. Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran;2. Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran |
| |
| Abstract: | We propose a topic-dependent attention model for sentiment classification and topic extraction. Our model assumes that a global topic embedding is shared across documents and employs an attention mechanism to derive local topic embedding for words and sentences. These are subsequently incorporated in a modified Gated Recurrent Unit (GRU) for sentiment classification and extraction of topics bearing different sentiment polarities. Those topics emerge from the words’ local topic embeddings learned by the internal attention of the GRU cells in the context of a multi-task learning framework. In this paper, we present the hierarchical architecture, the new GRU unit and the experiments conducted on users’ reviews which demonstrate classification performance on a par with the state-of-the-art methodologies for sentiment classification and topic coherence outperforming the current approaches for supervised topic extraction. In addition, our model is able to extract coherent aspect-sentiment clusters despite using no aspect-level annotations for training. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
