Sentiment classification method capable of combining Doc2vce with convolutional neural network

Abstract

The invention requests to protect a sentiment classification method capable of combining Doc2vce with a convolutional neural network, and effectively combines the Doc2vce with the CNN (Convolutional Neural Network). For characteristic representation, the combination method considers a semantic relationship between words, solves dimensionality disasters and also considers a sequence problem between words. The CNN can make up the deficiencies of a superficial characteristic learning method through learning one deep nonlinear network structure. The representation of input data is expressed in a distributed way, so that powerful characteristic learning capability is shown, characteristic extraction and mode classification can be simultaneously carried out, and two characteristics of the spare connection and the weight sharing of the CNN model can reduce the training parameters of the network, a neural network structure becomes simple and higher in adaptation. Since the Doc2ec and the CNN are combined to process a sentiment classification problem, the accuracy of sentiment classification can be obviously improved.

Description

technical field [0001] The invention belongs to the field of emotion classification methods, in particular to an emotion classification method combining Doc2vec and convolutional neural network. Background technique [0002] Sentiment analysis is a common application of natural language processing (NLP) methods, especially in classification methods aimed at extracting the emotional content of text. Sentiment classification has many useful practices, such as companies analyzing consumer feedback on products, or detecting negative reviews in online reviews. Common sentiment classification methods mainly include shallow learning methods such as support vector machines, maximum entropy, and random walks. The functions used in these methods in the modeling process are simple, the calculation method is relatively simple, easy to implement and the amount of calculation is small, under the condition of limited samples and calculation units, the ability to express complex functions ...

AI Technical Summary

Problems solved by technology

Although word2vec analyzes the semantic relationship between words well and solves the problem of dimensionality disaster, it does not take into account the order of words, so different sentences may have the same representation, resulting in misunderstandings. High judgment rate

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