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A New Approach in Bloggers Classification with Hybrid of K-Nearest Neighbor and Artificial Neural Network Algorithms


  • Department of Computer Engineering, Hacettepe University, Beytepe, Ankara, Turkey
  • Young Researchers and Elite Club, Dehdasht Branch, Islamic Azad University, Dehdasht, Iran, Islamic Republic of
  • Young Researchers and Elite Club, Urmia Branch, Islamic Azad University, Urmia, Iran, Islamic Republic of


Blogs are one of the effective tools of web2 which are considered as one of the major module and of social and interactive capabilities in making IT world wonderful for the cyber and virtual living. Two methods were used in this paper: K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANNs). These methods are classified based on Kohkiloye and Boyer Ahmad province bloggers dataset considering input features of each blogger to the other methods and previously provided algorithms as more optimal. Our simulation and experiments not only provide hopeful results but also higher anticipation and classification rate.


Artificial Neural Networks, Bloggers Classification, Decision Tree, K-Nearest Neighbor.

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  • Gharehchopogh FS, Khaze SR. Data mining application for cyber space tendency in blog writing: a case study. IJCA. 2012; 47(18):40-6.
  • Chang C-F. Exploring genres and mediated actions in Taiwanese college students' blog writing. The JALT CALL Journal. 2011; 7(2):137-50.
  • Tekinarslan E. Blogs: a qualitative investigation into an instructor and undergraduate students' experiences. Australas J Educ Tech. 2008; 24(4):402-12.
  • Abidin MJZ, Pour-mohammadi M, Hamid FBA. Blogging: promoting peer collaboration in writing. Int J Bus Humanit Tech. 2011; 1(3):98-105.
  • Karandikar A. Generative model to construct blog and post networks in blogosphere [M.Sc Thesis]. University of Maryland; 2007.
  • Sun Y-C, Chang Y-J. Blogging to learn: becoming EFL academic writers through collaborative dialogues. Lang Learn Tech. 2012; 16(1):43-61.
  • Arnot K. Blogging the Gap: A survey of China bloggers [M.Sc Thesis]. London School of Economics and Political Science; 2010 Aug.
  • Lin F, Cohen WW. The multi rank bootstrap algorithm: self-supervised political blog classification and ranking using semi-supervised link classification. ICWSM. Seattle, Washington, USA; 2008.
  • Ines B, Nicolas B, Mathieu R. Blog classification: adding linguistic knowledge to improve the K-NN Algorithm. Intelligent Information Processing 4. Springer; 2008. p. 68-77.
  • Jun Y, Park H, Myaeng SH. A hybrid mood classification approach for blog text. Trends in Artificial Intelligence. 2006; 4099:1099-103.
  • Sriphaew K, Takamura H, Okumura M. Cool blog classification from positive and unlabeled examples. Advances in Knowledge Discovery and Data Mining. Springer; 2009. p. 62-73.
  • Wiegand M, Klakow D. Topic-Related polarity classification of blog sentences. Progress in Artificial Intelligence. Springer; 2009. p. 658-69.
  • Rustagi M, Prasath RR, Goswami S, Sarkar S. Learning age and gender of blogger from stylistic variation. Pattern Recognition and Machine Intelligence. Springer; 2009. p. 205-12.
  • Yang S, Yan J, Gao C, Tan G. Blogger's interest mining based on chinese text classification. Nonlinear Mathematics for Uncertainty and its Applications. Springer; 2011. p. 611-8.
  • Ishino A, Nanba H, Takezawa T. Automatic classification of link polarity in blog entries. Information Retrieval Technology. Springer; 2011. p. 509-18.
  • Ayyasamy RK, Alhashmi SM, Eu-Gene S, Tahayna B. Enhancing concept based modeling approach for blog classification. Knowledge Engineering and Management, Part 5. Springer; 2012. p. 409-16.
  • Ayyasamy RK, Alhashmi SM, Eu-Gene S, Tahayna B. enhancing automatic blog classification using concept-category vectorization. Knowledge Engineering and Management, Part 5. Springer; 2012. p. 487-97.
  • Tsai FS. Blogger-Link-Topic model for blog mining. New Frontiers in Applied Data Mining. Springer; 2012. p. 28-39.
  • Yan X, Yan L. Gender Classification of Weblog. Proceedings of AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs. 2006. p. 228-30.
  • Sun A, Suryanto MA, Liu Y. Blog classification using tags: an empirical study. Asian Digital Libraries. Springer; 2007. p. 307-16.
  • Qu H, Pietra AL, Poon S. Automated Blog Classification: Challenges and Pitfalls. Proceeding of AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs. 2006. p. 184-6.
  • Elgersma E, de Rijke M. Personal vs non-personal blogs: initial classification experiments. SIGIR. New York, USA: ACM; 2008. p. 723-4.
  • Durant KT, Smith MD. Predicting the political sentiment of web log posts using supervised machine learning techniques coupled with feature selection. Philadelphia, USA: WEBKDD; 2006. p. 187-206.
  • Gerrish J, Qazvinian V, Shi X. Blog Classification with Co-training. 2007.
  • Mukherjee A, Liu B. Improving Gender Classification of Blog. EMNLP; 2010. p. 207-17.
  • Bhagat S, Rozenbaum I, Cormode G. Applying link-based classification to label blogs. WebKDD/SNA-KDD. New York, USA: ACM; 2007. p. 92-101.
  • Fix E, Hodges JL. Discriminatory analysis, nonparametric discrimination: Consistency properties. Technical Report 4. Randolph Field, Texas: USAF School of Aviation Medicine; 1951.
  • Silverman BW, Jones MC, Fix E, Hodges JL. An important contribution to nonparametric discriminant analysis and density estimation: commentary on fix and hodges. International Statistical Review. 1989; 57(3):233-8.
  • Phyu T. Survey of Classification Techniques in Data Mining. International Multi Conference of Engineers and Computer Scientists. 2009 Mar; Hong Kong.
  • He J, Tan A, Tan C. Comparative study on chinese text categorization methods. The PRICAI 2000 Workshop on Text and Web Mining. 2000. p. 25-31; Melbourne.
  • Han J. Data Mining: Concepts and Techniques. Morgan Kaufmann; 2006. p. 348-50.
  • Klair AS, Kaur RP. Software effort estimation using SVM and KNN. International Conference on Computer Graphics, Simulation and Modeling. 2012 Jul. p. 146-7; Thailand.
  • Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou Z-H, Steinbach M, Hand DJ, Steinberg D. Top 10 algorithms in data mining. Journal Knowledge and Information Systems. 2007 Dec; 14(1):1-37.
  • Gorunescu F. Data mining concepts, models and techniques. Intelligent Systems Reference Library. Springer; 2011. p. 256-60.
  • Gharehchopogh FS. Neural networks application in software cost estimation : a case study. International Symposium on Innovations in Intelligent Systems and Applications (INISTA); 2011; Istanbul. p. 69-73.
  • Gharehchopogh FS, Ahmadzadeh E. Artificial neural network application in letters recognition for farsi/arabic manuscripts. International Journal of Scientific and Technology Research. 2012 Sept; 1(8):90-4.
  • Zhang GP. Neural networks for classification: a survey. IEEE Trans Syst Man Cybern C Appl Rev. 2000 Nov; 30(4):451-62.
  • Reby D. Artificial neural networks as a classification method in the behavioral sciences. Behavioral Processes. 1997; 40: 35-43.
  • Svozil D. Introduction to multi-layer feed-forward neural networks. Chemometr Intell Lab Syst.1997; 39:43-62.
  • Towell GG, Shavlik JW. Knowledge-based artificial neural networks. Artif Intell.1994; 70(1-2):119-65.
  • Gharehchopogh FS, Khalifelu ZA. Neural network application in diagnosis of patient: a case study. International Conference on Computer Networks and Information Technology (ICCNIT); 2011; Abbottabad. p. 245-9.
  • Martin B. Instance-based learning: nearest neighbor with generalization. University of Waikato; 1995.
  • Larose DT. Discovering knowledge in data: an introduction to data mining. Wiley-Interscince; 2005.


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