References

Attia, M., L. Tounsi, P. Pecina, J.V. Genabith and A. Toral, 2010. Automatic extraction of Arabic multiword expressions. Proceedings of the the 7th Conference on Language Resources and Evaluation (LREC 2010), June 7, 2010, DORAS, Beijing, China, pp: 18-26.

Bouchekif, A. G. Damnati and D. Charlet, 2014. Intra-content term weighting for topic segmentation. Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 4-9, 2014, IEEE, Florence, Italy, pp: 7113-7117.

Carmel, D., A. Mejer, Y. Pinter and I. Szpektor, 2014. Improving term weighting for community question answering search using syntactic analysis. Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, November 3-7, 2014, ACM, New York, USA., ISBN: 978-1-4503-2598-1, pp: 351-360.

Chiang, D.A., H.C. Keh, H.H. Huang and D. Chyr, 2008. The Chinese text categorization system with association rule and category priority. Expert Syst. Appl., 35: 102-110.
CrossRef  |  Direct Link  |  

Choi, D., B. Ko, H. Kim and P. Kim, 2014. Text analysis for detecting terrorism-related articles on the web. J. Netw. Comput. Appl., 38: 16-21.
CrossRef  |  Direct Link  |  

Doko, A., M. Stula and D. Stipanicev, 2013. A recursive TF-ISF based sentence retrieval method with local context. Int. J. Mach. Learn. Comput., 3: 195-200.
Direct Link  |  

Fang, H., T. Tao and C. Zhai, 2011. Diagnostic evaluation of information retrieval models. ACM. Trans. Inf. Syst., 29: 1-49.
CrossRef  |  Direct Link  |  

Fayyad, U., G. Piatetsky-Shapiro and P. Smyth, 1996. From data mining to knowledge discovery in databases. AI Mag., 17: 37-54.
Direct Link  |  

Fayyad, U.M., Shapiro, G.P. and R. Uthurusamy, 2003. Summary from the KDD-03 panel: Data mining: The next 10 years. ACM. SIGKDD. Explorations Newsl., 5: 191-196.
CrossRef  |  Direct Link  |  

Gautam, J. and E. Kumar, 2013. An integrated and improved approach to terms weighting in text classification. IJCSI. Int. J. Comput. Sci. Issues, 10: 310-314.
Direct Link  |  

Goswami, P. and V. Kamath, 2014. The DF-ICF algorithm-modified TF-IDF. Int. J. Comput. Appl., 93: 28-30.
Direct Link  |  

Huo, W., 2012. Automatic multi-word term extraction and its application to web-page summarization. Ph.D. Thesis, The University of Guelph, Guelph, Ontario, Canada.

Keh, H.C., D.A. Chiang, C.C. Hsu and H.H. Huang, 2010. The chinese text categorization system with category priorities. J. Software, 5: 1137-1143.
CrossRef  |  Direct Link  |  

Khamar, K., 2013. Short text classification using kNN based on distance function. IJARCCE. Int. J. Adv. Res. Comput. Commun. Eng., 2: 1916-1919.
Direct Link  |  

Lan, M., C.L. Tan and H.B. Low, 2006. Proposing a new term weighting scheme for text categorization. Proceedings of the 21st National Conference on Artificial Intelligence, pp: 763-768.

Lan, M., C.L. Tan, J. Su and Y. Lu, 2009. Supervised and traditional term weighting methods for automatic text categorization. Pattern Anal. Mach. Intell. IEEE. Trans., 31: 721-735.
CrossRef  |  PubMed  |  Direct Link  |  

Lee, D.L., H. Chuang and K. Seamons, 1997. Document ranking and the vector-space model. Software IEEE., 14: 67-75.
CrossRef  |  Direct Link  |  

Liangtu, S. and Z. Xiaoming, 2007. Web text feature extraction with particle swarm optimization. IJCSNS. Int. J. Comput. Sci. Netw. Secur., 7: 132-136.
Direct Link  |  

Liu, L. and T. Peng, 2014. Clustering-based method for positive and unlabeled text categorization enhanced by improved TFIDF. J. Inf. Sci. Eng., 30: 1463-1481.
Direct Link  |  

Liu, Y., Y. Wang, L. Feng and X. Zhu, 2014. Term frequency combined hybrid feature selection method for spam filtering. Pattern Anal. Appl., 1: 1-15.
CrossRef  |  Direct Link  |  

Lyman, P. and H.R. Varian, 2003. How much information 2003? http://www2.sims.berkeley.edu/research/projects/how-much-info-2003/.

Murray, G. and S. Renals, 2007. Term-Weighting for Summarization of Multi-Party Spoken Dialogues. In: Machine Learning for Multimodal Interaction. Popescu, B.A., S. Renals, B. Herve (Eds.). Springer Berlin Heidelberg, Berlin, Germany, pp: 156-167.

Paik, J.H., 2013. A novel TF-IDF weighting scheme for effective ranking. Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 28-August 01, 2013, ACM, New York, USA., ISBN: 978-1-4503-2034-4, pp: 343-352.

Pesaranghader, A., N. Mustapha and N.M. Sharef, 2013. Improving multi-term topics focused crawling by introducing Term Frequency-Information Content (TF-IC) measure. Proceedings of the 2013 International Conference on Research and Innovation in Information Systems (ICRIIS), November 27-28, 2013, IEEE, Kuala Lumpur, Malaysia, ISBN: 978-1-4799-2486-8, pp: 102-106.

Porter, M.F., 1980. An algorithm for suffix stripping. Program Electron. Lib. Inform. Syst., 14: 130-137.
CrossRef  |  Direct Link  |  

Raj, R.G., 2012. Improving the relevancy of document search using the multi-term adjacency keyword-order model. Malaysian J. Comput. Sci., 25: 1-10.
Direct Link  |  

Ricardo, R.M.B., 2013. Ranking of multi-word terms. Master Thesis, Leiden Institute of Advanced Computer Science, Leiden University, Netherlands

Sabbah, T. and A. Selamat, 2014. Modified Frequency-Based Term Weighting Scheme for Accurate Dark Web Content Classification. In: Information Retrieval Technology. Jaafar, A., N.M. Ali, , S.A.M. Noah, A.F. Smeaton and P. Bruza et al. (Eds.). Springer International Publishing, New York, USA., pp: 184-196.

Sabbah, T., A. Selamat, M.H. Selamat, R. Ibrahim and H. Fujita, 2016. Hybridized term-weighting method for dark web classification. Neurocomput., 173: 1908-1926.
CrossRef  |  Direct Link  |  

Salton, G., 1989. Automatic Text Processing: The Transformation, Analysis and Retrieval of Information by Computer. Addison-Wesley, Boston, MA., USA., ISBN: 9780201122275, Pages: 530.

Sanderson, M. and I. Ruthven, 1996. Report on the glasgow IR group (glair4) submission. Proceedings of the Fifth Text Retrieval Conference (TREC-5), November 20-22, 1996, Gaithersburg, Maryland, USA., pp: 517-520.

Santhanakumar M. and C.C. Columbus, 2015. Various improved TFIDF schemes for term weighting in text categorization: A survey. Int. J. Appl. Eng. Res., 10: 11905-11910.

Santhanakumar, M. and C.C. Columbus, 2015. Web usage based analysis of web pages using RapidMiner. WSEAS. Trans. Comput., 14: 455-464.

Selamat, A. and S. Omatu, 2003. Neural networks for web page classification based on augmented PCA. Proceedings of the International Joint Conference on Neural Networks 2003, July, 20-24, 2003, IEEE, New Jersey, USA., pp: 1792-1797.

Vivekanandan, M.V. and M.N. Karpagavalli, 2014. Efficient data analysis scheme for increasing performance in big data. Int. J. Res. Sci. Technol., 1: 193-198.
Direct Link  |  

Wang D. and H. Zhang, 2013. Inverse-category-frequency based supervised term weighting scheme for text categorization. J. Inf. Sci. Eng., 29: 209-225.

Wang, N., P. Wang and B. Zhang, 2010. An improved TF-IDF weights function based on information theory. Proceedings of the 2010 International Conference on Computer and Communication Technologies in Agriculture Engineering (CCTAE), June 12-13, 2010, IEEE, Chengdu, China, pp: 439-441.

Wang, Y., Y. Liu, L. Feng and X. Zhu, 2015. Novel feature selection method based on harmony search for email classification. Knowledge Based Syst., 73: 311-323.
CrossRef  |  Direct Link  |  

Xia, T. and Y. Chai, 2011. An improvement to TF-IDF: Term distribution based term weight algorithm. J. Software, 6: 413-420.
CrossRef  |  Direct Link  |  

Zaefarian, R., J. Siddiqi, B. Akhgar and G. Zaefarian, 2006. A new algorithm for term weighting in text summarization process. Proceedings of the 6th WSEAS International Conference on Applied Informatics and Communications, August 18-20, 2006, World Scientific and Engineering Academy and Society (WSEAS), Corfu Island, Greece., pp: 292-297.

Zulkifeli, W.W., N. Mustapha and A. Mustapha, 2012. Classic term weighting technique for mining web content outliers. Int. Conferenceon Comput. Tech. Artif., 1: 271-275.
Direct Link  |