Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Text Fine-grained Sentiment Generation Method Based on Probabilistic Reasoning and Sentiment Cognition

A probabilistic reasoning, fine-grained technology, applied in electrical digital data processing, natural language data processing, instruments, etc., can solve problems such as ignoring implicit emotions

Active Publication Date: 2021-09-21
福昕鲲鹏(北京)信息科技有限公司
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Aiming at the defects of the prior art, the present invention proposes a text fine-grained emotion generation method based on probabilistic reasoning and emotion cognition, using the emotion cognition method to solve the problem of ignoring implicit emotions in other emotion generation methods, and using Bayeux The Si network calculates the probability of emotion generation and improves the accuracy of emotion category generation

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Text Fine-grained Sentiment Generation Method Based on Probabilistic Reasoning and Sentiment Cognition
  • Text Fine-grained Sentiment Generation Method Based on Probabilistic Reasoning and Sentiment Cognition
  • Text Fine-grained Sentiment Generation Method Based on Probabilistic Reasoning and Sentiment Cognition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0103] Text: / / @微博 Funny Ranking: Gulangyu is really good, let's go on a trip together! [rejoice]

[0104] 1) Prepare the text data set S required for the training method with a total of 15,000 items, and mark the text according to seven emotions: like, happiness, fear, anger, disgust, sadness, and surprise.

[0105] 2) The text data set S is analyzed, part-of-speech tagging, dependency syntactic analysis, semantic dependency and other processing using the language cloud technology platform of Harbin Institute of Technology.

[0106] 3) Use the emotional evaluation variables extracted in step 3 above to construct the Bayesian network.

[0107] 4) According to the characteristics of the network text, determine the emotional evaluation variables based on emoticons , where exp indicates whether there are emoticons, and other variables indicate the corresponding emotional types. 1, yes 2; determined based on the statistical word frequency emotional evaluation variables indicate...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The present invention relates to a text fine-grained emotion generation method based on probabilistic reasoning and emotional cognition, comprising the following steps: Step 1: Prepare the text data set required by the training method; Step 2: Process the text data set; Step 3: Extract and construct Sentiment evaluation variables used by the Bayesian network; Step 4: According to the characteristics of the network text, add emotional evaluation variables based on emoji and word frequency; Step 5: Build an emotional knowledge base; Step 6: Build a common sense knowledge base; Step 7: Emotion Evaluation variable assignment; Step 8: Learning the network structure of the Bayesian network for emotion generation; Step 9, parameter learning; Step 10: Complete the construction of the emotion generation method. The present invention uses the emotion recognition method to solve the problem of ignoring the hidden emotion existing in other emotion generation methods, and uses the Bayesian network to calculate the probability of emotion generation, compares the probability of each emotion category, and generates one or more emotion.

Description

technical field [0001] The present invention relates to the field of machine learning and natural language processing text sentiment analysis, in particular to the field of emotion cognition methods and Bayesian network reasoning ideas, and in particular to a text fine-grained emotion generation method based on probabilistic reasoning and emotion cognition. Background technique [0002] Text sentiment analysis is a popular research field in natural language processing, mainly to automatically identify the emotional categories expressed by users in text. Emotion is a psychological and physiological state produced by a variety of feelings, thoughts, and behaviors. It generally refers to likes, dislikes, anger, surprise, pride, etc. Traditional sentiment analysis is mainly to identify the user's emotional tendency: positive, negative or neutral, while sentiment analysis is a fine-grained sentiment analysis task that can identify multiple emotions. The methods of emotion genera...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06F40/205G06F40/247
CPCG06F40/205G06F40/247
Inventor 柴玉梅徐源音王黎明张卓韩飞韩慧李永帅
Owner 福昕鲲鹏(北京)信息科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com