Name
Abstract | ||
|---|---|---|
Conversational sentiment analysis (CSA) is emergent research field in natural language processing (NLP). This brings a lot of new issues worth studying and directions worth exploring. At the same time, there are many difficulties that need to be overcome. There are lot of challenges, such as lacking of effective deep learning models and being short of appropriate datasets. Inspired by the concept of density matrix in quantum mechanics, we propose a novel attention mechanism called DMATT and apply it to conversational sentiment analysis tasks. In the experiment, we find that deep learning model combined with DMATT has a great improvement in test results compared to the model with traditional attention mechanism. Recurrent neural networks (RNN) and their variants LSTM and GRU are very effective choices in solving time series problem such as conversational sentiment analysis tasks. In this paper, we propose a new model combining GRU and DMATT called DMATT-BiGRU. We experiment in multiple datasets, one of which is called ScenarioSA collected by ourselves. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICTAI.2019.00232 | 2019 IEEE 31ST INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2019) |
Keywords | Field | DocType |
conversational sentiment analysis, density matrix, attention | Quantum,Computer science,Sentiment analysis,Recurrent neural network,Artificial intelligence,Deep learning,Machine learning | Conference |
ISSN | Citations | PageRank |
1082-3409 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Guo | 1 | 0 | 0.34 |
| Junwei Zhang | 2 | 0 | 1.01 |
| Yuexian Hou | 3 | 269 | 38.59 |
| Xiujun Gong | 4 | 0 | 0.34 |
| Panpan Wang | 5 | 20 | 5.75 |
| Yazhou Zhang | 6 | 23 | 8.02 |