Two new feature extraction methods for text classification: TESDF and SADF

Kılıç, Erdal; Ateş, N.; Karakaya, Aykut; Şahin, Durmuş Özkan

doi:10.1109/siu.2015.7129862

Cited by 6 publications

(4 citation statements)

References 5 publications

(6 reference statements)

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“…Kilic et al [21] used academic articles as a dataset and measured the success of classifying the articles by employing text mining techniques. The dataset created for feature selection was composed of abstracts of 2000 articles from 50 different journals.…”

Section: Related Workmentioning

confidence: 99%

A real-time social network-based knowledge discovery system for decision making

Yüksel

Tan

2018

Automatika

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The increasing amount of data in social networks has complicated data processing and interpretation. Therefore, intelligent decision-support mechanisms that have the ability to automatically extract meaning from data and interpret the opinions of people in real time have become inevitable. In this study, an intelligent multilingual decision support system was implemented, and a new algorithm that employs text mining and sentiment analysis techniques was developed to automatically interpret the opinions of social network users about the places they plan to visit. The system can be used as a baseline for sentiment analysis in social networks and can be adapted to build new systems. In this study, we set our main focus on Turkish language and show the applicability of our approach for other languages through the experiments for English language. The dataset required for the implementation of text mining techniques was created based on the venue recommendations shared on Foursquare social media platform. As a result, a contribution was made to the way the social network users make decisions without reading thousands of recommendations. Our results show that the developed system achieves classification accuracy of 84.49% for Turkish and 95% for English. Finally, the most liked or disliked foods/beverages are correctly identified for 107 out of 128 venues.

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Section: Related Workmentioning

confidence: 99%

A real-time social network-based knowledge discovery system for decision making

Yüksel

Tan

2018

Automatika

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“…There are currently two methods used to reduce the feature dimension: feature selection and feature extraction (see, e.g. Kilic, 2015). Feature selection selects features that have the best ability to distinguish categories based on certain rules to form a new subset in the original feature space.…”

Section: Text Classification Problemsmentioning

confidence: 99%

A method of dimensionality reduction by selection of components in principal component analysis for text classification

Yang-wu

Zong

2018

Filomat

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Dimensionality reduction, including feature extraction and selection, is one of the key points for text classification. In this paper, we propose a mixed method of dimensionality reduction constructed by principal components analysis and the selection of components. Principal components analysis is a method of feature extraction. Not all of the components in principal component analysis contribute to classification, because PCA objective is not a form of discriminant analysis (see, e.g. . In this context, we present a function of components selection, which returns the useful components for classification by the indicators of the performances on the different subsets of the components. Compared to traditional methods of feature selection, SVM classifiers trained on selected components show improved classification performance and a reduction in computational overhead.

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“…Veri büyük oldugu için Varyasyonel Bayes yöntemiyle çıkarsama yapan Ç evrimiçi SDA [13] yöntemi kullanılmıştır 4 . Model egitildikten sonra, gözetimli sınıflandırmada kullanılmaküzere, her konu bir kategoriye atanmıştır.…”

Section: B Saklı Dirichlet Atamasıunclassified

“…Kılıç ve arkadaşlarının çalışmasında TF-TDF'nın iki yeni varyasyonu tanıtılmış ve Türkçe derlemde başarımın arttıgı gösterilmiştir [4]. Ay ve arkadaşlarının çalışmasında genetik algoritma kullanılmış ve yeni bir nitelik agırlıklandırma yöntemi sunulmuştur [5].…”

Section: Introductionunclassified

Document classification of SuDer Turkish news corpora

Şen

Yanıkoğlu

2018

2018 26th Signal Processing and Communications Applications Conference (SIU)

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Ozetçe -Kelime Temsil Vektörleri, Dogal Dilİşleme alanındaki çeşitli problemlere başarılı bir şekilde uygulanmaktadır; ancak bu vektörleri egitmek için büyük miktarda metin verisi gereklidir.İngilizce için metin derlemi pek çok farklı konu ve boyut için rahatlıkla bulunsa da, Türkçe için az sayıda derlem bulunmaktadır. Bu çalışmada iki çevrimiçi haber sitesinden büyük miktarlı metin derlemleri toplanmış ve etiket olarak internet sayfalarında bulunan kategori bilgisi kullanılmıştır. Oluşturulan derlemler çeşitli doküman sınıflandırma modelleri ile denenmiştir. Temsil vektörleri kullanan modellerin, geleneksel TF-TDFözniteliklerini kullanan yöntemlerden daha iyi sonuç verdigi görülmüştür. Aynı anda hem kelime vektörlerini hem de doküman sınıflandırmasınıögrenen bir yapay sinir agı en iyi sonucu vermiştir.Anahtar Kelimeler-doküman sınıflandırma, SuDer haber metinleri, kelime temsil vektörleri, yapay sinir agları Abstract-Word embeddings are successfully employed in various Natural Language Processing tasks, but training them requires large amount of text, which is scarce for Turkish. In this work, we collected large amounts of articles from two news websites and tags within web pages are used as labels. Obtained corpora are tested with various document classification models. Embedding based models performed better than models with the traditional TF-IDF features. A neural network that simultaneously learns the word embeddings and document classification performed the best.

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Two new feature extraction methods for text classification: TESDF and SADF

Cited by 6 publications

References 5 publications

A real-time social network-based knowledge discovery system for decision making

A real-time social network-based knowledge discovery system for decision making

A method of dimensionality reduction by selection of components in principal component analysis for text classification

Document classification of SuDer Turkish news corpora

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