Short text aspect-level sentiment classification method

Abstract

The invention discloses a short text aspect-level sentiment classification method, which comprises the following steps of: 1, performing word segmentation on a short text, generating word vectors through words in the short text, and labeling all aspect vectors and part-of-speech vectors; 2, judging the owned aspects of the short text by using an XLNET model, and splicing the word vector, the part-of-speech vector and the owned aspect vector of each word in sequence; 3, inputting the vector formed by splicing the words in the step 2 into an emotion classification Bilstm model, inputting the obtained implicit vector of each word into an Attention mechanism, and returning the weight of the implicit vector of each word; and 4, performing weighted average by using the implicit vector and the weight corresponding to each word, enabling a result to enter a softmax neural network to obtain a corresponding sentiment, and taking a relatively high probability value as a sentiment classification result. Fifferent sentiments of the short text in different aspects can be recognized, so that fine-grained sentiment classification is completed.

Description

technical field [0001] The invention belongs to the field of natural language processing in artificial intelligence, and specifically relates to an emotion classification method of a Bilstm model that integrates word vectors, aspect vectors, and part-of-speech vectors, and adds an Attention mechanism. Background technique [0002] With the development of e-commerce platforms, short text comments have increasingly become an important way for users to express their emotions. However, more than one aspect is often involved in short texts, and even opposite emotional attitudes may be held for different aspects. At present, most of the sentiment classifications for short texts are coarse-grained classifications, that is, only one sentiment classification is given for short texts, and the sentiments corresponding to different aspects cannot be fine-grained. Contents of the invention [0003] In order to solve the shortcomings of the above-mentioned prior art, the present invent...

AI Technical Summary

Problems solved by technology

At present, most of the sentiment classifications for short texts are coarse-grained classifications, that is, only one sentiment classification is given for short texts, and the sentiments corresponding to different aspects cannot be fine-grained.

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