Financial text sentiment analysis method

Abstract

The invention relates to a financial text sentiment analysis method, comprising the following operational steps of: firstly, constructing a financial sentiment dictionary; secondly, performing sentence segmentation on a text, performing word segmentation, and generating a word segmentation sequence vector comprising a word text, a word property and a word sentiment value; thirdly, correcting the influence of a negative word, a degree word, a single concept word, a transitional word, a standard word and the like on the sentiment value; fourthly, calculating a fused financial text sentiment value by using weighted combination of a multiplication sentiment model for calculation of a sentiment generation function and an addition sentiment model for words in articles; and fifthly, compatibly expressing sentiment values [0,2] and [-1,1]. According to the method, for different sentiment environments, an input layer is applied as a word, a hidden layer is applied as a sentence sentiment layer expressed by the sentiment generation function, and an output layer is applied as a neural network of a nerve cell to calculate financial sentiment.

Description

technical field [0001] The invention relates to the fields of natural language processing and artificial intelligence, in particular to a financial text sentiment analysis method. Background technique [0002] With the popularity of the Internet, people's lives have also undergone great changes. The Internet has gradually become the carrier of various information in society. Especially with the continuous development of China's economy, financial products such as stocks and national bonds have gradually become hot topics for people to discuss. More and more people obtain financial, financial, and other economic information through the Internet. News and related information. Web text has also become an important source for us to obtain information, express opinions and exchange emotions. More and more people like to exchange their opinions on the Internet, so there are a lot of tendentious text information on the Internet. [0003] The general sentiment analysis is to iden...

AI Technical Summary

Problems solved by technology

The established classification model has a lot to do with the specific field, and the model built for one field is usually not suitable for another field, and the construction of the emotional word dictionary is time-consuming and laborious

There is a certain amount of noise in the news, which affects the quality of the training samples, making the trained model vulnerable to the noise, and ultimately affects the accuracy and recall of the classification

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