Cycle nerve network text emotion analysis method by embedding logic rules

Abstract

The invention provides a cycle nerve network text emotion analysis method by embedding logic rules, and the method comprises the steps of grabbing a text corpus for training and conducting emotion class labeling; then dividing the emotion labeled text corpus into a training set corpus and a test set corpus for dividing the words and removing the stopped words; then conducting training to the training set corpus and the test set corpus whose words are divided and stopped words are removed by using word2vec algorithm so as to obtain a corresponding word vector; inputting the training set corpus and the test set corpus in an existing knowledge base and analyzing in combination with a probability graph model; embedding a first order logic rule in the cycle nerve network through a logic cycle nerve network structure (Logic-RNN and Logic-LSTM). According to the invention, the training direction of the cycle nerve network can be controlled, and inclination to human's intuition is realized; besides, the precision of text emotion analysis is improved. Therefore, the method can be used in other fields like natural language processing and machine learning.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a text sentiment analysis method for embedding logic rules in Recurrent Neural Networks (RNNs). Background technique [0002] With the development of Internet technology and the rise of web2.0, the Internet has gradually changed from a static information carrier to a platform for people to obtain information, express opinions, and exchange emotions. People share, comment, and express their opinions on various things online. , Opinions, such as comments on movies, news, stocks, etc. These comments are self-evident for the importance of the government, enterprises, consumers, etc. However, with the explosive growth of online comment data, relying on manual processing of massive text data Acquisition, processing, analysis, and prediction are impractical, so using automated tools to quickly obtain valuable information from a large amount of text has become an urgent need for ...

AI Technical Summary

Problems solved by technology

[0007] As a sequence model, the cyclic neural network has achieved great success and is widely used in many natural language processing tasks, such as language recognition, machine translation, sentiment analysis, entity recognition, etc., which makes more and more people believe in the cyclic neural network. It can be a good language model, but there are still many shortcomings in the cyclic neural network. For example, the training of the cyclic neural network takes a lot of time, and the high-precision model depends on a large amount of data. Simple data learning often leads to inexplicable sex and counterintuitive

Images