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User emotion analysis method based on multi-model fusion

A sentiment analysis, multi-model technology, applied in the field of information systems, can solve problems such as low accuracy and difficult analysis methods

Pending Publication Date: 2020-06-09
BEIJING UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the difficulty and low precision of the existing analysis methods, and to provide a new microblog emotion classification method based on hybrid learning

Method used

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  • User emotion analysis method based on multi-model fusion
  • User emotion analysis method based on multi-model fusion
  • User emotion analysis method based on multi-model fusion

Examples

Experimental program
Comparison scheme
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Embodiment Construction

[0021] Step 1: Preprocess the text first, using stutter word segmentation in python. This word segmentation tool is very useful, and it can analyze the part of speech at the same time as word segmentation.

[0022] Step 2: Use stop words to remove some meaningless words, and train the word vector after word segmentation through a sentiment dictionary, including emotional words, negative words, adverb degrees, and stop words.

[0023] Step 3: Assign values ​​to emotional words. Positive emotional words have a score of 1, negative emotional words have a score of -1, and neutral words have a score of 0. The degree of adverbs can also be based on different levels given in the dictionary. value, all negative words are set to -1.

[0024] Step 4: Sentence processing

[0025] Microblog texts usually consist of multiple sentences, which means that the emotional tendency of the text is affected by multiple sentences. Opposing conjunctions appear in different positions, leading to a s...

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Abstract

The invention relates to a new sentiment classification model based on hybrid learning. In the first stage, an improved dictionary classification method is used for calculating emotion scores on a whole data set, and data with extremely high scores or extremely low scores are directly marked; in the second stage, the rest is used to calculate the emotion score based on the emotion dictionary and the BI-GRU fusion model, and the two-stage hybrid framework enables the method to be effectively applied to emotion classification. Experiments show that a single model is unsatisfactory in emotion classification effect under various complex contexts, high in difficulty and low in precision, and error preference of the single model can be effectively improved by adopting a multi-model fusion method, so that the classification effect is improved.

Description

technical field [0001] The invention relates to the field of information systems, in particular to a user emotion analysis method based on the combination of emotion dictionary and deep learning. It is especially suitable for sentiment analysis of texts such as Weibo and Moments published on social networks. Background technique [0002] With the rise of social networks such as Weibo and WeChat, the Internet has not only become an important source for people to obtain information, but also a platform for people to express their opinions. By commenting on hot events, expressing film review opinions, and describing product experience in online communities such as Weibo, a large amount of text information with emotional tendencies is generated. [0003] By performing sentiment analysis on these text information, it is possible to better understand user behavior, find out the user's inclination to products, and the degree of attention to hot events. With the rapid increase of ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/211G06F40/242G06F40/289G06N3/04G06N3/08
CPCG06N3/08G06N3/045
Inventor 赵德群王昊
Owner BEIJING UNIV OF TECH
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