Research on Emotional Tendencies of College Students Based on Self-Attention and Bi-LSTM
The performance of the network model based on the word vector relies heavily on the accuracy of word segmentation,a method of sentiment analysis for college students based on FastText character vector combined with Self-Attention and BiLSTM is proposed.Firstly,character vectors are generated using the fasttext model,then contextual semantic features are extracted by the bidirectional long and short-term memory model and key information is strengthened using the Self-Attention mechanism,finally,the sentiment categories are judged us-ing the Softmax classifier.The experimental results show that character vector is more suitable for short text than word vector,and character-SATT-BiLSTM has achieved better classification results than character-LSTM,character-BiLSTM and other models.The classification perfor-mance can be increased by 6%and 3%,respectively.
FastTextcharacter vectorbidirectional long short-term memoryself-attentionemotional tendency analysis
国家科技期刊平台
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