Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Text sentiment classification method facing Chinese Web comments

A technology for sentiment classification and text, applied in the field of data processing

Inactive Publication Date: 2013-05-22
WUXI NANLIGONG TECH DEV +1
View PDF0 Cites 85 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But at present, neither English nor Chinese has a complete dictionary covering the semantic orientation of words, and it is impossible to have such a complete dictionary, because many words have different semantic orientations in different contexts

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 sentiment classification method facing Chinese Web comments
  • Text sentiment classification method facing Chinese Web comments
  • Text sentiment classification method facing Chinese Web comments

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0035] The overall process of text sentiment classification is as follows: figure 1 shown. The whole process can be divided into two parts: training process and classification process.

[0036] The basic flow of the training process is: training text preprocessing → feature selection → vectorized representation of text → training classifier. The specific processing is as follows:

[0037] 1. Given a manually classified training text set , to perform some preprocessing on it, such as Chinese word segmentation, stop word filtering, etc.

[0038] 2. Use statistics such as frequency to calculate the category of entries in the text The distribution in , after feature selection, get the local features of this category. Set the set of selected feature words ,in for category in the first feature words, Indicates the total number of feature words in this category. The union of the local feature word sets of all categories The set of global feature words that constitu...

Embodiment 2

[0052] Embodiment 2, vector space model

[0053] The Vector Space Model (Vector Space Model, VSM), proposed by Salton et al. of Harvard University in 1975, was first applied as an indexing method.

[0054] The basic idea of ​​VSM is to use Bag of words (Bow) to represent text, and each entry is used as one dimension of the feature space coordinate system, and the text is regarded as a vector of feature space, and the angle between the two vectors is used to measure the similarity between two texts.

[0055] In VSM, each document is mapped to a point in a vector space spanned by a set of canonically orthogonalized feature vectors. Assuming that the set consisting of n feature items is F=(t1,t2,...,tn), the document is formalized as a vector di=(wi1,wi2,...,wik,...,win ), wik represents the weight of the kth feature item entry tk of di. The value of each dimension of the vector represents the weight of the feature item in the document, which is used to describe the importance...

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 invention belongs to the field of data processing technology and discloses a text sentiment classification method facing Chinese Web comments. The text sentiment classification method includes a training process and a classification process. The training process includes the steps of carrying out training text preprocessing, carrying out feature selecting, carrying out vectorization representation of a text and obtaining a training classifier. The classification process includes the steps of carrying out test text preprocessing, carrying out feature selecting, utilizing the classifier to classify and outputting a classification result. On the basis of an original document classification method, document frequency (DF) and information gain (IG) are used and a sentiment dictionary of negative words, degree adverbs and dynamic sentiment words are built to distinguish sentiment tendency of Chinese feature words, select feature words, calculate a feature weight value and build a feature vector. Moreover, a NaiveBayes classification algorithm is used for training to obtain the classifier, carrying out sentiment classification on the text, providing effective data mining for users and then carrying out analysis processing.

Description

[0001] technical field [0002] The invention belongs to the technical field of data processing, and in particular relates to a text emotion classification method for Chinese Web comments. Background technique [0003] As an important information interaction medium, the main function of text is to express emotions. Content-based research has been very mature. In recent years, more and more studies have begun to focus on "expression", that is, sentiment analysis. The main research contents include Semantic orientation recognition of words, sentiment-based text classification, opinion extraction, subjectivity analysis, etc. For a document, the main thing that can play a decisive role in its semantic orientation is the words used to make up the document. Therefore, the basis of sentiment-based text classification is to determine the semantic orientation of words. But at present, neither English nor Chinese has a complete dictionary covering the semantic orientation of words, ...

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
IPC IPC(8): G06F17/30G06F17/27
Inventor 李千目倪铭印杰侯君
Owner WUXI NANLIGONG TECH DEV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
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