A Deep Learning Method Based Self-Attention and Bi-directional LSTM in Emotion Classification,ERICDATA高等教育知識庫
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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 FeiYuanbo ZhuQuanzhu YaoQingzheng XuBo 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 classificationSelf-AttentionDeep learningRNNBi-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 10.3966/160792642020092105019   複製DOI
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