A fuzzy conceptualization model for text mining with application in opinion polarity classification

Sheng-Tun Li, Fu Ching Tsai

Research output: Contribution to journalArticle

37 Citations (Scopus)

Abstract

Automatic text classification in text mining is a critical technique to manage huge collections of documents. However, most existing document classification algorithms are easily affected by ambiguous terms. The ability to disambiguate for a classifier is thus as important as the ability to classify accurately. In this paper, we propose a novel classification framework based on fuzzy formal concept analysis to conceptualize documents into a more abstract form of concepts, and use these as the training examples to alleviate the arbitrary outcomes caused by ambiguous terms. The proposed model is evaluated on a benchmark testbed and two opinion polarity datasets. The experimental results indicate superior performance in all datasets. Applying concept analysis to opinion polarity classification is a leading endeavor in the disambiguation of Web 2.0 contents, and the approach presented in this paper offers significant improvements on current methods. The results of the proposed model reveal its ability to decrease the sensitivity to noise, as well as its adaptability in cross domain applications.

Original languageEnglish
Pages (from-to)23-33
Number of pages11
JournalKnowledge-Based Systems
Volume39
DOIs
Publication statusPublished - 2013 Feb 1

Fingerprint

Formal concept analysis
Testbeds
Classifiers
Conceptualization
Text mining
Classifier
Document classification
Adaptability
Benchmark
Web 2.0
Text classification

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Software
  • Information Systems and Management
  • Artificial Intelligence

Cite this

@article{3e006b37443443ab83b87eea7c89d2d7,
title = "A fuzzy conceptualization model for text mining with application in opinion polarity classification",
abstract = "Automatic text classification in text mining is a critical technique to manage huge collections of documents. However, most existing document classification algorithms are easily affected by ambiguous terms. The ability to disambiguate for a classifier is thus as important as the ability to classify accurately. In this paper, we propose a novel classification framework based on fuzzy formal concept analysis to conceptualize documents into a more abstract form of concepts, and use these as the training examples to alleviate the arbitrary outcomes caused by ambiguous terms. The proposed model is evaluated on a benchmark testbed and two opinion polarity datasets. The experimental results indicate superior performance in all datasets. Applying concept analysis to opinion polarity classification is a leading endeavor in the disambiguation of Web 2.0 contents, and the approach presented in this paper offers significant improvements on current methods. The results of the proposed model reveal its ability to decrease the sensitivity to noise, as well as its adaptability in cross domain applications.",
author = "Sheng-Tun Li and Tsai, {Fu Ching}",
year = "2013",
month = "2",
day = "1",
doi = "10.1016/j.knosys.2012.10.005",
language = "English",
volume = "39",
pages = "23--33",
journal = "Knowledge-Based Systems",
issn = "0950-7051",
publisher = "Elsevier",

}

A fuzzy conceptualization model for text mining with application in opinion polarity classification. / Li, Sheng-Tun; Tsai, Fu Ching.

In: Knowledge-Based Systems, Vol. 39, 01.02.2013, p. 23-33.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A fuzzy conceptualization model for text mining with application in opinion polarity classification

AU - Li, Sheng-Tun

AU - Tsai, Fu Ching

PY - 2013/2/1

Y1 - 2013/2/1

N2 - Automatic text classification in text mining is a critical technique to manage huge collections of documents. However, most existing document classification algorithms are easily affected by ambiguous terms. The ability to disambiguate for a classifier is thus as important as the ability to classify accurately. In this paper, we propose a novel classification framework based on fuzzy formal concept analysis to conceptualize documents into a more abstract form of concepts, and use these as the training examples to alleviate the arbitrary outcomes caused by ambiguous terms. The proposed model is evaluated on a benchmark testbed and two opinion polarity datasets. The experimental results indicate superior performance in all datasets. Applying concept analysis to opinion polarity classification is a leading endeavor in the disambiguation of Web 2.0 contents, and the approach presented in this paper offers significant improvements on current methods. The results of the proposed model reveal its ability to decrease the sensitivity to noise, as well as its adaptability in cross domain applications.

AB - Automatic text classification in text mining is a critical technique to manage huge collections of documents. However, most existing document classification algorithms are easily affected by ambiguous terms. The ability to disambiguate for a classifier is thus as important as the ability to classify accurately. In this paper, we propose a novel classification framework based on fuzzy formal concept analysis to conceptualize documents into a more abstract form of concepts, and use these as the training examples to alleviate the arbitrary outcomes caused by ambiguous terms. The proposed model is evaluated on a benchmark testbed and two opinion polarity datasets. The experimental results indicate superior performance in all datasets. Applying concept analysis to opinion polarity classification is a leading endeavor in the disambiguation of Web 2.0 contents, and the approach presented in this paper offers significant improvements on current methods. The results of the proposed model reveal its ability to decrease the sensitivity to noise, as well as its adaptability in cross domain applications.

UR - http://www.scopus.com/inward/record.url?scp=84871924091&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84871924091&partnerID=8YFLogxK

U2 - 10.1016/j.knosys.2012.10.005

DO - 10.1016/j.knosys.2012.10.005

M3 - Article

AN - SCOPUS:84871924091

VL - 39

SP - 23

EP - 33

JO - Knowledge-Based Systems

JF - Knowledge-Based Systems

SN - 0950-7051

ER -