Name
Title | ||
|---|---|---|
Sentiment Analysis Based on Attention Mechanisms and Bi-Directional LSTM Fusion Model. |
Abstract | ||
|---|---|---|
This Sentiment analysis is mainly found in the useru0027s social platform for a hot event or product point of view and attitude. Most existing sentiment analysis approaches heavily rely on a large amount of labeled data that usually involve time-consuming and error-prone manual annotations. In order to avoid the dependence on the manual annotation dictionary and reduce the human intervention in the machine learning process, In this paper, Based on the researches on sentiment analysis and deep learning, we propose a hybrid framework AM-Bi-LSTM that combines Attention Mechanism and Bi-directional Long-Short-Term Memory (Bi-LSTM) neural networks for sentence classification. We demonstrate the effectiveness and efficiency of our approach on a representative Stanford Sentiment Treebank (SST) dataset. For SST-1 and SST-2, Compared with the currently published state-of-the-art methods Conv-RNN, the accuracy of AM-Bi-LSTM is improved by 2.787% and 1.946% respectively. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00177 | SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mei Wang | 1 | 0 | 0.34 |
| Yangyang Zhu | 2 | 0 | 0.34 |
| Shulin Liu | 3 | 0 | 0.34 |
| Chunfeng Song | 4 | 3 | 3.08 |
| Zheng Wang | 5 | 0 | 0.34 |
| Pai Wang | 6 | 3 | 2.07 |
| Xuebin Qin | 7 | 32 | 7.95 |