Method for judging emotional tendentiousness of short text

Abstract

The invention discloses a method for judging emotional tendentiousness of a short text. The method comprises the following steps of: A. directly building an emotional dictionary from texts to be analyzed; B. according to the emotional dictionary, judging the emotional polarity of the texts to be analyzed based on a local dependency relationship. The invention aims to provide a feasible scheme which is suitable for texts with non-strict syntaxes, and which can obtain the emotional polarity of phrase collocation. Firstly, the method for automatically building the emotional dictionary does not depend on a large number of external annotation samples, but the emotional dictionary is directly built by data to be analyzed, so that the defects brought by emotional dictionary opening and field content difference can be overcome; secondly, the method adopts a local dependency relationship for analysis, can be suitable for texts with non-strict sentence structures, especially, short texts, and the single characteristic of the invention comprises collocation information, therefore, the method can be applied to the situation that the same emotional identifier words have different emotional polarities in collocation of different contexts.

Description

technical field [0001] The invention relates to the field of word processing, and more specifically, relates to a method for judging the emotional tendency of short texts. Background technique [0002] One of the main techniques for analyzing the semantics of a large number of short texts is emotional polarity judgment, which is often applied to the discourse content of certain products, services, and events. Use this as a way to determine whether people's reactions to the topic are rants or applause. For enterprises, the emotional polarity of text information, especially commentary short text information, directly reflects consumers' feedback on enterprise services, and can be used to assist relevant decision makers to respond in a targeted manner. [0003] To keep up with the massive amount of information being generated, automated sentiment classification methods are often used. The basic method for judging the emotional polarity of short texts is: 1. Refer to the p...

AI Technical Summary

Problems solved by technology

The main problem with this scheme is that the weighted summation method ignores the syntactic structure and context collocation of emotional words, which will affect the accuracy of the final emotional orientation judgment

[0012] In practical applications, there is an endogenous paradox in the field of sentiment analysis: if syntactic analysis is not used, the matching relationship between the evaluation object and the evaluation content cannot be obtained, and it is easy to misjudge the polarity; if syntactic analysis is used, it is impossible to deal with a large number of existing Syntactically imprecise web short texts

If you don’t use externally labeled samples or sentiment dictionaries, you can’t start training sentiment classifiers; if you use externally labeled samples or emotional words, you can’t guarantee the degree of correlation between external data and the data to be analyzed, and possible misjudgments

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