Attention dual-layer LSTM-based long text emotional tendency analysis method

Abstract

The invention relates to an attention dual-layer LSTM-based long text emotional tendency analysis method, belongs to the field of natural language processing and machine learning, and mainly aims to solve the problem of difficulty in accurately judging an emotional tendency of a full text due to long comment length of the long text, discrete distribution of positive and negative emotional featuresand different emotional semantic contribution degrees of sentences. The method comprises the steps of firstly learning sentence-level emotional vector representation by utilizing LSTM; secondly coding semantic relationships between emotional semantics of all the sentences in a document and the sentences by adopting bidirectional LSTM, and based on an attention mechanism, performing weight allocation on the sentences with different emotional semantic contribution degrees; and finally, weighting the sentence-level emotional vector representation to obtain document-level emotional vector representation of the long text, and through a Softmax layer, obtaining the emotional tendency of the long text. An experiment is performed in Yelp2015 and IMDb film comment corpora; and a result shows thata relatively good classification effect can be achieved, so that the emotional classification correctness is further improved.

Description

technical field [0001] The invention relates to an attention-based double-layer LSTM method for analyzing the emotional tendency of long texts, and belongs to the fields of natural language processing and machine learning. Background technique [0002] The sentiment analysis method of long text comments has gradually begun to use deep learning methods, among which deep learning methods represented by CNN, RNN and LSTM have achieved good results in the field of sentiment analysis. [0003] 1. CNN-based method [0004] Although the CNN-based algorithm can effectively classify text, the problem of text sentiment analysis is not a simple text classification problem. Because both the training corpus and the prediction corpus are texts containing the author's emotions, it is necessary to consider the text context connection to achieve accurate discrimination of the emotional tendency of long texts. Therefore, an ideal text sentiment analysis algorithm needs to consider recording...

AI Technical Summary

Problems solved by technology

[0010] The purpose of the present invention is to solve the problem that the length of long text reviews is long, the positive and negative emotional features are discretely distributed, and

Images