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.
ChenSYLiuX.The contribution of data mining to information science. Journal of Information Science2004; 30(6): 550–558.
2.
AmancioDR. A systematic comparison of supervised classifiers. PloS One2014; 9(4): 94–137.
3.
MichieDSpiegelhalterDJTaylorCC.Machine learning, neural and statistical classification. New York: Ellis Horwood Limited, 1994.
4.
SebastianiF.Machine learning in automated text categorization. ACM Computing Surveys2002; 34(5): 1–47.
5.
JiemingYZhaoyangQLiuZ. Improved feature-selection method considering the imbalance problem in text categorization. The Scientific World Journal2014; doi:10.1155/2014/625342.
6.
OnanA.Classifier and feature set ensembles for web page classification. Journal of Information Science2015; doi: 10.1177/0165551515591724.
7.
WolpertDHMacreadyWG.No free lunch theorem for search. Technical Report SFI-TR-05–010, Santa Fe Institute, 1995.
8.
ReadJ. Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the ACL student research workshop, 2005, pp. 43–48.
9.
ZhangPHeZ.Using data-driven feature enrichment of text representation and ensemble technique for sentence-level polarity classification. Journal of Information Science2015; doi: 10.1177/0165551515585264.
10.
CavnarWBTrenkleJM.N-gram based text categorization. In: Proceedings of SDAIR-94, 3rd annual symposium on document analysis and information retrieval, 1994, pp. 161–175.
11.
IsmailH. Automatic Arabic text categorization: A comprehensive comparative study. Journal of Information Science2015; 41(1): 114–124.
12.
ShaalanKOudahM.A hybrid approach to Arabic named entity recognition. Journal of Information Science2014; 40(1): 67–87.
13.
Al-RadaidehQAAlEroudAFAl-ShawakfaEM.A hybrid approach to detecting alerts in Arabic e-mail messages. Journal of Information Science2012; 38(1): 87–99.
14.
GüranAAkyokusSGülerNGürbüzZ.Turkish text categorization using n-gram words In
Proceedings of the international symposium on innovations in intelligent systems and applications (INISTA), 2009, pp. 369–373.
15.
TorunoğluDÇakırmanEGanizMC. Analysis of preprocessing methods on classification of Turkish texts. In: Proceedings of international symposium on innovations in intelligent systems and applications, 2011, pp. 112–118.
16.
AkkusBKRuketC. Categorization of Turkish news documents with morphological analysis. In: Proceedings of the ACL student research workshop, 2013, pp. 1–8.
17.
AmasyalıMFBekenA. Measurement of Turkish word semantic similarity and text categorization application. In: Proceedings of IEEE signal processing and communications applications conference, Antalya, Turkey, 9–11 April 2009. New York: IEEE, pp. 1–4.
18.
AmasyalıMFDiriB. Automatic Turkish text categorization in terms of author, genre and gender. In: Natural language processing and information systems. Berlin: Springer, 2006, pp. 221–226.
19.
TüfekçiPUzunE. Author detection by using different term weighting schemes. In: Proceedings of IEEE signal processing and communications applications conference (SIU), Trabzon, Turkey, 24–26 April 2013. New York: IEEE, pp. 1–4.
20.
ÇataltepeZTuranYKesginF. Turkish document classification using shorter roots. In: Proceedings of IEEE signal processing and communications applications conference (SIU), Eskisehir, Turkey, 11–13 June 2007. New York: IEEE, pp. 1–4.
21.
AlparslanEKarahocaABahşiH.Classification of confidential documents by using adaptive neurofuzzy inference systems. Procedia Computer Science2011; 3: 1412–1417.
22.
UysalAKGunalS.The impact of preprocessing on text classification. Information Processing and Management2014; 50: 104–112.
23.
GunalS.Hybrid feature selection for text classification. Turkish Journal of Electrical Engineering and Computer Sciences2012; 20: 1296–1311.
24.
ÖzalpNYılmazGAyanU. Novel comment filtering approach based on outlier on streaming data. In: Proceedings of IEEE signal processing and communications applications conference (SIU), Mugla, Turkey, 18–20 April 2012. New York: IEEE, pp. 1–4.
CanFKocberberSBalcikEKaynakCOcalanHCVursavasOM.Information retrieval on Turkish texts. Journal of the American Society for Information Science and Technology2008; 59(3): 407–421.
27.
KılıçaslanYGünerESYıldırımS.Learning-based pronoun resolution for Turkish with a comparative evaluation. Computer Speech and Language2009; 23: 311–331.
28.
SaltonGChristopherB.Term-weighting approaches in automatic text retrieval. Information Processing and Management1988; 24: 513–523.
29.
AkinAAAkinMD. Zemberek, an open source NLP framework for Turkic Languages. Structure2007; 10: 1–5.
30.
YildirimPBirantD. Naive Bayes classifier for continuous variables using novel method (NBC4D) and distributions. In: Proceedings of IEEE international symposium on innovations in intelligent systems and applications (INISTA) proceedings, Alberobello, Italy23–25 June 2014. New York: IEEE, pp. 110–115.
31.
SebastianiF. Text categorization. In: Proceedings of Text Mining and its Applications to Intelligence, CRM and Knowledge Management, Southampton, UK, 2005. WIT Press, pp.109–129.
QuinlanJR. C4.5: Programs for machine learning. Machine Learning1993; 16(3): 235–240.
34.
XuBGuoXYeYChengJ.An improved random forest classifier for text categorization. Journal of Computers2012; 7(12): 2913–2920.
35.
HallM.Correlation-based feature selection for machine learning. PhD thesis, Department of Computer Science, University of Waikato, New Zealand, 1999, pp. 51–74.
36.
YangYPedersenJO. A comparative study on feature selection in text categorization. In: Proceedings of the 14th international conference on machine learning (ICML ‘97), Nashville, TN, 1997. San Francisco, CA: Morgan Kaufmann, pp. 412–420.
37.
YounEJeongMK.Class dependent feature scaling method using naive Bayes classifier for text datamining. Pattern Recognition Letters2009; 30(5): 477–485.
38.
WittenIHFrankE.Data mining: Practical machine learning tools and techniques. San Francisco, CA: Morgan Kaufman, 2005.
39.
AmasyalıM. Text2arff: Automatic feature extraction software for Turkish texts. In: Signal Processing and communications applications conference (SIU), Diyarbakir, 22–24 April 2010. New York: IEEE, pp. 629–632.
40.
KohaviRProvostF.On applied research in machine learning. Machine Learning1998; 30(2–3): 127–132.
41.
KevinB. When is ‘nearest neighbor’ meaningful. In: 7th International conference on database theory, Jerusalem, 10–12 January 1999. New York: IEEE, pp. 217–235.
42.
MeijingLXiumingYRyuKH.MapReduce-based web mining for prediction of web-user navigation. Journal of Information Science2014; 40(5): 557–567.
