Novel comment filtering approach based on outlier on streaming data

Ozalp, Nuri; Yılmaz, Güray; Ayan, Uğur

doi:10.1109/siu.2012.6204765

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…There are also some other Turkish text analyses and text retrieval studies other than those performed on Turkish TC. Özalp et al [24] conducted studies to detect slang words in news and comments made for articles and columns on the Internet. They proposed a study that can automatically filter comments made for online articles, magazines and news texts on the Internet.…”

Section: Related Workmentioning

confidence: 99%

TTC-3600: A new benchmark dataset for Turkish text categorization

Kılınç

Özçift

Bozyiğit

et al. 2015

Journal of Information Science

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Owing to the rapid growth of the World Wide Web, the number of documents that can be accessed via the Internet explosively increases with each passing day. Considering news portals in particular, sometimes documents related to categories such as technology, sports and politics seem to be in the wrong category or documents are located in a generic category called others. At this point, text categorization (TC), which is generally addressed as a supervised learning task is needed. Although there are substantial number of studies conducted on TC in other languages, the number of studies conducted in Turkish is very limited owing to the lack of accessibility and usability of datasets created. In this paper, a new dataset named TTC-3600, which can be widely used in studies of TC of Turkish news and articles, is created. TTC-3600 is a well-documented dataset and its file formats are compatible with well-known text mining tools. Five widely used classifiers within the field of TC and two feature selection methods are evaluated on TTC-3600. The experimental results indicate that the best accuracy criterion value 91.03% is obtained with the combination of Random Forest classifier and attribute ranking-based feature selection method in all comparisons performed after pre-processing and feature selection steps. The publicly available TTC-3600 dataset and the experimental results of this study can be utilized in comparative experiments by other researchers.

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Section: Related Workmentioning

confidence: 99%

TTC-3600: A new benchmark dataset for Turkish text categorization

Kılınç

Özçift

Bozyiğit

et al. 2015

Journal of Information Science

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“…Özalp et al [23] perform a preliminary study for automatic fltering of comments for news and articles. The study involves two steps: 1. creating rules for word extraction where the words that are not fully compatible with spelling rules are fltered out, 2. developing a fast and accurate classifcation algorithm.…”

Section: Turkish Text Classifcationmentioning

confidence: 99%

Tuning Language Representation Models for Classification of Turkish News

Tokgoz

Turhan

Bölücü

et al. 2021

2021 International Symposium on Electrical, Electronics and Information Engineering

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Pre-trained language representation models have proven to be efcient in learning a universal language representation regardless of the natural language processing task to be performed. The language representation models such as BERT and DistilBERT have achieved amazing results in many language understanding tasks. Studies on text classifcation problem in the literature are generally carried out for the English language. This study aims to classify the news in the Turkish language using pre-trained language representation models. In this study, we utilize BERT and DistilBERT by tuning both models for the text classifcation task to learn the categories of Turkish news with diferent tokenization methods. We provide a quantitative analysis of the performance of BERT and DistilBERT on the Turkish news dataset by comparing the models in terms of their representation capability in the text classifcation task. The highest performance is obtained with DistilBERT with an accuracy of 97.4%.

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