Emotion discrimination method based on fine-grained annotation data

Abstract

The invention relates to an emotion discrimination method based on fine-grained annotation data. The method comprises the following steps: collecting financial news data, dividing news data into a labeled sample set and an unlabeled sample set; training a first classifier and a second classifier through the labeled sample set and the unlabeled sample set; the first classifier can screen out the key sentences in the article; the second classifier judges the emotional tendency of the article; model parameters of the first classifier and model parameters of the second classifier are obtained respectively; and adding the data with high confidence in the classification result into the annotation sample set, selecting the most worthy annotated data C from the unannotated sample set by using an active learning theory, sending the most worthy annotated data C to a worker for annotation, and circularly training the emotion discrimination model until the classification precision is achieved, andending the training to obtain the discrimination model.

Description

technical field [0001] The invention relates to the field of text processing, in particular to an emotion discrimination method based on fine-grained labeled data. Background technique [0002] In the current era of information overload, the speed of news generation far exceeds the speed that individuals can process. In order to ensure that users can obtain effective information, proper feature extraction and filtering of original news has become a relatively common and necessary practice. When using mathematical models to quantify financial news, the emotional tendency (positive / negative / neutral) of the news is one of the very important attributes. [0003] There are three ideas for sentiment classification of news texts: sentiment lexicon-based methods, machine learning-based methods, and deep learning-based methods. [0004] The method based on sentiment lexicon mainly judges the sentiment polarity of the text by constructing sentiment lexicon and a series of rules; from...

AI Technical Summary

Problems solved by technology

However, the existing methods ignore a problem in the actual process, that is, only a small number of sentences in financial news are effective for sentiment classification, and most sentences are useless information or noise information

In the case of retaining a large amount of useless information and noise, in order to achieve a high classification accuracy, the workload of labeling will undoubtedly increase significantly.

Images