Sentiment classification method capable of combining Doc2vce with convolutional neural network
A technology of convolutional neural network and emotion classification, which is applied in the field of emotion classification combining Doc2vec and convolutional neural network, can solve the problems of not considering the problem of word and word order, dimensionality disaster, high misjudgment rate, etc., to improve accuracy rate, strong adaptability, and the effect of reducing training parameters
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[0019] Below in conjunction with accompanying drawing, the present invention is further described:
[0020] like figure 1 shown, as figure 1 As shown, the specific steps of the emotion classification method combining Doc2vec and CNN of the present invention are:
[0021] Step 1: Collect emotional text corpus, and manually label the categories. For example, the text label of positive emotion is 1, and the text label of negative emotion is 2. And remove the leading and trailing spaces of the text, and represent the data in the text as a sentence, which is convenient for subsequent processing. The corpus is divided into training set and test set. The training set is used to train the sentiment classification model, and the test set is used to test the effect of the model classification.
[0022] Step 2: First, collect sentiment dictionary, which is the basic resource of text sentiment analysis, which is actually a collection of sentiment words. In a broad sense, it refers to...
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