Class-indexing-based term weighting for automatic text classification

Ren, Fuji; Sohrab, Mohammad Golam

doi:10.1016/j.ins.2013.02.029

Cited by 115 publications

(97 citation statements)

References 39 publications

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“…Even so, STW seems to be more reasonable and promising than TF-IDF. Later, STW has attracted a lot of interest from researchers, e.g., Lan, Tan, Su, and Lu (2009) , Altinçay and Erenel (2010), Liu, Loh and Sun (2009), Wang and Zhang (2013), Ren and Sohrab (2013), Nguyen, Chang, and Hui (2013) , Peng, Liu, and Zuo (2014) , and Deng et al (2014) , etc. Term weighting becomes one of the hot research topics in text classification and various new STW schemes have been proposed from time to time.…”

Section: Introductionmentioning

confidence: 99%

Turning from TF-IDF to TF-IGM for term weighting in text classification

Chen

Zhang

Long

et al. 2016

Expert Systems with Applications

244

144

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Section: Introductionmentioning

confidence: 99%

Turning from TF-IDF to TF-IGM for term weighting in text classification

Chen

Zhang

Long

et al. 2016

Expert Systems with Applications

244

144

View full text Add to dashboard Cite

“…CW (ConfWeigh) istatistiksel güven aralıklarına dayalı metin sınıflandırma için önerilmiş bir yöntemdir [43]. DTFIDF (Delta-TFIDF) duygu içeren terimlerin ağırlığını artıran ve duygu analizi çalışmaları için öneren bir yöntemdir [44]. CITW (Class Indexing based Term Weighting) ilgili sınıftaki sık gözlenen ve gözlenmeyen terimlerin ayırt edilmesini sağlayan bir yöntemdir [45].…”

Section: Yeni Yaklaşımlarunclassified

The impact of term weighting method on Twitter sentiment analysis

Çoban¹,

Özyer²

2018

Pamukkale J Eng Sci

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“…The results showed an enhancements in the accuracy of prediction. Ren and Sohrab (2013) have introduced class-indexing-based termweighting approaches. The proposed class-based indexing is incorporated with term, document and class index.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Combined Feature Selection Methods for Arabic Text Classification

Adel¹,

Omar²,

Al-Shabi³

2014

Journal of Computer Science

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Text classification is a very important task due to the huge amount of electronic documents. One of the problems of text classification is the high dimensionality of feature space. Researchers proposed many algorithms to select related features from text. These algorithms have been studied extensively for English text, while studies for Arabic are still limited. This study introduces an investigation on the performance of five widely used feature selection methods namely Chi-square, Correlation, GSS Coefficient, Information Gain and Relief F. In addition, this study also introduces an approach of combination of feature selection methods based on the average weight of the features. The experiments are conducted using Naïve Bayes and Support Vector Machine classifiers to classify a published Arabic corpus. The results show that the best results were obtained when using Information Gain method. The results also show that the combination of multiple feature selection methods outperforms the best results obtain by the individual methods.

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Class-indexing-based term weighting for automatic text classification

Cited by 115 publications

References 39 publications

Turning from TF-IDF to TF-IGM for term weighting in text classification

Turning from TF-IDF to TF-IGM for term weighting in text classification

The impact of term weighting method on Twitter sentiment analysis

A Comparative Study of Combined Feature Selection Methods for Arabic Text Classification

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