閱讀全文 | |
篇名 |
A Deep Learning Method Based Self-Attention and Bi-directional LSTM in Emotion Classification
|
---|---|
並列篇名 | A Deep Learning Method Based Self-Attention and Bi-directional LSTM in Emotion Classification |
作者 | Rong Fei、Yuanbo Zhu、Quanzhu Yao、Qingzheng Xu、Bo Hu |
英文摘要 | Traditional recurrent neural network cannot achieve parallelism, while convolutional neural network cannot be used to process variable-length sequence samples directly. In this study, we combined the bidirectional short-time memory (Bi-LSTM) model with the selfattention to form the SA-BiLSTM method, to further improve the performance of the emotion classification model. The SA-BiLSTM method obtains the attention probability distribution by calculating the correlation between the intermediate state and final state. The SABiLSTM method weights the state of each moment differently to ensure that the problem of information redundancy is solved while retaining valid information and the accuracy of text classification is improved by optimizing the text feature vector. Experimental results on three different data sets show that the performance of SA-BiLSTM algorithm outperforms the six emotion classification methods by the accuracy, loss rate, time and other performance indicators of the classification model. |
起訖頁 | 1447-1461 |
關鍵詞 | Sentiment classification、Self-Attention、Deep learning、RNN、Bi-LSTM |
刊名 | 網際網路技術學刊 |
期數 | 202009 (21:5期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
|
QR Code | |
該期刊 上一篇
| An Enhanced PROMOT Algorithm with D2D and Robust for Mobile Edge Computing |
該期刊 下一篇
| Research on MTCNN Face Recognition System in Low Computing Power Scenarios |