Comment sentiment analysis method based on improved neural network

Abstract

The invention discloses a comment sentiment analysis method based on an improved neural network, and the method comprises the steps: constructing a comment representation matrix for inputted comment text data; calculating a comment representation matrix through a plurality of convolution kernels in sequence to obtain feature maps of different sizes; calculating each obtained feature map by using pyramid pooling to obtain a feature vector with a fixed length; splicing to obtain a feature vector of each feature map, and connecting the feature vectors with the full connection layer; mapping the output of the full connection layer into a probability distribution vector by using a Softmax function, wherein each dimension of the probability distribution vector corresponds to an emotion categoryin the emotion analysis task; and selecting the emotion category corresponding to the value with the maximum probability in the probability distribution vector as a comment emotion judgment result. According to the method, the text sequence can be effectively modeled, and the sequence characteristics of the text can be effectively reserved, so that the emotional attitude in the comment text content can be accurately and effectively recognized.

Description

technical field [0001] The invention belongs to the technical field of text recognition, in particular to a comment emotion analysis method based on an improved neural network. Background technique [0002] The purpose of review sentiment analysis is to make the computer "understand" the reviewer's emotional attitude towards the reviewed product through an intelligent method. Traditional sentiment analysis algorithms mainly use statistical methods to extract the inherent characteristics of text in terms of word frequency, sequence, etc., and then use classic statistical learning algorithms (such as Bayesian, support vector machines, decision trees, etc.) analyze. Traditional sentiment analysis algorithms mostly rely on bag-of-words models or Markov assumptions. The bag-of-words model only considers information related to word frequency and ignores the sequence relationship between words, so it cannot perform sequence modeling effectively. The Markov hypothesis believes th...

AI Technical Summary

Problems solved by technology

The bag-of-words model only considers information related to word frequency and ignores the sequence relationship between words, so it cannot effectively perform sequence modeling

The Markov hypothesis believes that "the appearance of a word is only related to its first k words", so it cannot solve the problem of long sequence modeling

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