Microblog emotion analysis method based on standard dictionaries and semantic rules

A sentiment analysis and microblog technology, applied in the field of pattern recognition, can solve the problems of ignoring the contextual relationship of words and syntactic rules, and the effect of microblog short text analysis is not ideal, so as to achieve the effect of high classification accuracy

Inactive Publication Date: 2016-12-07
BEIJING UNIV OF TECH
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Problems solved by technology

However, in the process of analyzing sentiment, traditional algorithms still face some important problems to be solved: 1) Although the semantic sentiment analysis algorithm can isolate words from sentences, it ignores the

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  • Microblog emotion analysis method based on standard dictionaries and semantic rules
  • Microblog emotion analysis method based on standard dictionaries and semantic rules
  • Microblog emotion analysis method based on standard dictionaries and semantic rules

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Example Embodiment

[0021] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0022] like figure 1 As shown, an embodiment of the present invention provides a microblog sentiment analysis method based on standard dictionaries and semantic rules, including

[0023] The following steps:

[0024] Step 1. Collect Weibo dataset

[0025] Collect 10,000 Weibo data from Sina Weibo data, and manually score the sentiment tendency value of each Weibo; the sentiment polarity is divided into positive, negative and neutral, and the scoring zone is [-1, 1] between.

[0026] Step 2. Do normalized text preprocessing on the microblog data

[0027] Perform text preprocessing on the collected microblog data, delete special characters and remove microblog emoticons in the text, and uniformly divide the microblog text into the part containing only microblog expressions and the plain text that is conducive to program analysis. part, and per...

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Abstract

The invention discloses a microblog emotion analysis method based on standard dictionaries and semantic rules. The microblog emotion analysis method comprises the following steps: collecting microblog data and manually labeling and marking the emotion value of each microblog; proposing corresponding standard micrblog emotion dictionaries, and establishing an emotion dictionary database; based on the standard emotion dictionaries, adding the semantic rules for assistance, and performing parameter adjustment and optimization on parameters of the semantic rules; based on a real dataset experiment, acquiring the final classification accuracy and precision. The technical scheme provided by the invention is adopted to well analyze the emotion tendency of each microblog user by introducing the standard emotion dictionaries, microblog expression dictionaries and the semantic rules, therefore, higher classification accuracy and precision are achieved.

Description

technical field [0001] The invention belongs to pattern recognition methods, in particular to a microblog emotion analysis method based on standard dictionaries and semantic rules. Background technique [0002] With the rapid development of network technology and the emergence of social media, network users experience unprecedented convenience. Social media (such as Facebook, Twitter, Sina Weibo, etc.) provide users with a platform for sharing information and publicly expressing personal opinions. With the huge amount of Weibo data behind it, there is a lot of information behind it. If these data can be effectively used, it can be Obtain huge potential value: for consumers, the summary analysis of certain types of product reviews can provide them with purchasing references; for commercial companies, analyzing product reviews can be used as the basis for their subsequent marketing strategy improvement; for government departments It is said that mastering the development of p...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06F17/27G06Q50/00
CPCG06F16/951G06F40/30G06Q50/01
Inventor 方超姚海鹏赵天奇王越乔
Owner BEIJING UNIV OF TECH
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