A text emotion analysis method based on dual-channel model
A sentiment analysis, dual-channel technology, applied in text database clustering/classification, biological neural network model, unstructured text data retrieval, etc., can solve the problem of insufficient extraction of text features, performance impact, inability to learn text Deep information features and other issues to achieve the effect of improving classification accuracy, enhancing influence, and reducing interference
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[0052] Examples:
[0053] This example uses real Chinese comments collected from the Internet and uses a text sentiment analysis method based on a dual-channel model to analyze text sentiment. The specific steps are as follows:
[0054] 1. Preprocess the data set, use stuttering word segmentation for word segmentation, remove stop words, and set the text length to 60;
[0055] 2. Assign label 1 to positive emotion text, assign label 0 to negative emotion text, and divide the test set and training set;
[0056] 3. Use the Word2Vec tool to train the word vector, set the dimension to 128, and splice the word vector to 60 according to the text word order 128 word vector matrix;
[0057] 4. Use the word vector matrix as the input features of the CNN and LSTM networks respectively, where the size of the convolution kernel of CNN is set to 3, 4, 5, the number is 128, and the number of hidden layer neurons of the LSTM network is set to 128;
[0058] 5. After the CNN and LSTM networks are connec...
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