Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Despite the rapid growth of other types of social media, Internet discussion forums remain a highly popular communication channel and a useful source of text data for analyzing user interests and sentiments. Being suited to richer, deeper, and longer discussions than microblogging services, they particularly well reflect topics of long-term, persisting involvement and areas of specialized knowledge or experience. Discovering and characterizing such topics and areas by text mining algorithms is therefore an interesting and useful research direction. This work presents a case study in which selected classification algorithms are applied to posts from a Polish discussion forum devoted to psychoactive substances received from home-grown plants, such as hashish or marijuana. The utility of two different vector text representations is examined: the simple bag of words representation and the more refined embedded global vectors one. While the former is found to work well for the multinomial naive Bayes algorithm, the latter turns out more useful for other classification algorithms: logistic regression, SVMs, and random forests. The obtained results suggest that post-classification can be applied for measuring publication intensity of particular topics and, in the case of forums related to psychoactive substances, for monitoring the risk of drug-related crime.
Rocznik
Tom
Strony
787--801
Opis fizyczny
Bibliogr. 66 poz., tab., wykr.
Twórcy
autor
- Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
Bibliografia
- [1] Aggarwal, C.C. and Zhai, C.-X. (Eds.) (2012). Mining Text Data, Springer, New York, NY.
- [2] Aswani Kumar, C. and Srinivas, S. (2006). Latent semantic indexing using eigenvalue analysis for efficient information retrieval, International Journal of Applied Mathematics and Computer Science 16(4): 551–558.
- [3] Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances, Philosophical Transactions of the Royal Society of London 53: 370–418.
- [4] Bilski, A. and Wojciechowski, J. (2016). Automatic parametric fault detection in complex analog systems based on a method of minimum node selection, International Journal of Applied Mathematics and Computer Science 26(3): 655–668, DOI: 10.1515/amcs-2016-0045.
- [5] Blei, D.M., Ng, A.Y. and Jordan, M.I. (2003). Latent Dirichlet allocation, Journal of Machine Learning Research 3: 993–1022.
- [6] Breiman, L. (1996). Bagging predictors, Machine Learning 24(2): 123–140.
- [7] Breiman, L. (2001). Random forests, Machine Learning 45(1): 5–32.
- [8] Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. (1984). Classification and Regression Trees, Chapman and Hall, New York, NY.
- [9] Cestnik, B. (1990). Estimating probabilities: A crucial task in machine learning, Proceedings of the 9th European Conference on Artificial Intelligence (ECAI-90), Stockholm, Sweden, pp. 147–149.
- [10] Cichosz, P. (2015). Data Mining Algorithms: Explained Using R, Wiley, Chichester.
- [11] Cortes, C. and Vapnik, V.N. (1995). Support-vector networks, Machine Learning 20(3): 273–297.
- [12] Cristianini, N. and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, New York, NY.
- [13] Dařena, F. and Žižka, J. (2017). Ensembles of classifiers for parallel categorization of large number of text documents expressing opinions, Journal of Applied Economic Sciences 12(1): 25–35.
- [14] Dietterich, T.G. (2000). Ensemble methods in machine learning, Proceedings of the 1st International Workshop on Multiple Classifier Systems, Cagliari, Italy, pp. 1–15.
- [15] Domingos, P. and Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss, Machine Learning 29(2–3): 103–137.
- [16] Duchi, J., Hazan, E. and Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization, Journal of Machine Learning Research 12: 2121–2159.
- [17] Dumais, S.T. (2005). Latent semantic analysis, Annual Review of Information Science and Technology 38(1): 188–229.
- [18] Dumais, S.T., Platt, J.C., Heckerman, D. and Sahami, M. (1998). Inductive learning algorithms and representations for text categorization, Proceedings of the 7th International Conference on Information and Knowledge Management (CIKM-98), Bethesda, MD, USA, pp. 148–155.
- [19] Egan, J.P. (1975). Signal Detection Theory and ROC Analysis, Academic Press, New York, NY.
- [20] Fawcett, T. (2006). An introduction to ROC analysis, Pattern Recognition Letters 27(8): 861–874.
- [21] Forman, G. (2003). An extensive empirical study of feature selection measures for text classification, Journal of Machine Learning Research 3: 1289–1305.
- [22] Goldberg, Y. and Levy, O. (2014). word2vec Explained: Deriving Mikolov et al.’s negative sampling word-embedding method, arXiv: 1402.3722.
- [23] Guyon, I.M. and Elisseeff, A. (2003). An introduction to variable and feature selection, Journal of Machine Learning Research 3: 1157–1182.
- [24] Hamel, L.H. (2009). Knowledge Discovery with Support Vector Machines, Wiley, New York, NY.
- [25] Hand, D.J. and Yu, K. (2001). Idiot’s Bayes—not so stupid after all?, International Statistical Review 69(3): 385–399.
- [26] Heaps, H.S. (1978). Information Retrieval: Computational and Theoretical Aspects, Academic Press, New York, NY.
- [27] Hilbe, J.M. (2009). Logistic Regression Models, Chapman and Hall, New York, NY.
- [28] Holtz, P., Kronberger, N. and Wagner, W. (2012). Analyzing Internet forums: A practical guide, Journal of Media Psychology 24(2): 55–66.
- [29] Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features, Proceedings of the 10th European Conference on Machine Learning (ECML-98), Chemnitz, Germany, pp. 137–142.
- [30] Joachims, T. (2002). Learning to Classify Text by Support Vector Machines: Methods, Theory, and Algorithms, Springer, New York, NY.
- [31] Koprinska, I., Poon, J., Clark, J. and Chan, J. (2007). Learning to classify e-mail, Information Sciences: An International Journal 177(10): 2167–2187.
- [32] Lau, J.H. and Baldwin, T. (2016). An empirical evaluation of doc2vec with practical insights into document embedding generation, Proceedings of the 1st Workshop on Representation Learning for NLP, Berlin, Germany, pp. 78–86.
- [33] Le, Q.V. and Mikolov, T. (2014). Distributed representations of sentences and documents, Proceedings of the 31st International Conference on Machine Learning (ICML-14), Beijing, China, pp. 1188–1196.
- [34] Lewis, D.D. (1998). Naive (Bayes) at forty: The independence assumption in information retrieval, Proceedings of the Tenth European Conference on Machine Learning (ECML-98), Chemnitz, Germany, pp. 4–15.
- [35] Liaw, A. and Wiener, M. (2002). Classification and regression by random Forest, R News 2(3): 18–22, http://CRAN.R-project.org/doc/Rnews/.
- [36] Liu, H. and Motoda, H. (1998). Feature Selection for Knowledge Discovery and Data Mining, Springer, New York, NY.
- [37] Liu, H., Motoda, H., Setiono, R. and Zhao, Z. (2010). Feature selection: An ever-evolving frontier in data mining, Proceedings of the 4th Workshop on Feature Selection in Data Mining (FSDM-10), Hyderabad, India, pp. 4–13.
- [38] Lui, A. K.-F., Li, S.C. and Choy, S.O. (2007). An evaluation of automatic text categorization in online discussion analysis, Proceedings of the 7th IEEE International Conference on Advanced Learning Technologies (ICALT-2007), Niigata, Japan, pp. 205–209.
- [39] Manning, C.D., Raghavan, P., and Schütze, H. (2008). Introduction to Information Retrieval, Cambridge University Press, Cambridge.
- [40] Marra, R.M., Moore, J.L. and Klimczak, A.K. (2004). Content analysis of online discussion forums: A comparative analysis of protocols, Educational Technology Research and Development 52(2): 23–40.
- [41] McCallum, A. and Nigam, K. (1998). A comparison of event models for naive Bayes text classification, Proceedings of the AAAI/ICML-98 Workshop on Learning for Text Categorization, Madison, WI, USA, pp. 41–48.
- [42] Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A. and Leisch, F. (2015). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien, R package version 1.6-7, https://CRAN.R-project.org/package=e1071.
- [43] Mikolov, T., Chen, K., Corrado, G.S. and Dean, J. (2013a). Efficient estimation of word representations in vector space, arXiv:1301.3781.
- [44] Mikolov, T., Le, Q.V. and Sutskever, I. (2013b). Exploiting similarities among languages for machine translation, arXiv:1309.4168.
- [45] Mitchell, J. and Lapata, M. (2010). Composition in distributional models of semantics, Cognitive Science 34(8): 1388–1429.
- [46] Moldovan, A., Bot¸, R.I. and Wanka, G. (2005). Latent semantic indexing for patent documents, International Journal of Applied Mathematics and Computer Science 15(4): 551–560.
- [47] Oooms, J. (2016). hunspell: Morphological Analysis and Spell Checker for R, R package version 2.3, https://CRAN.R-project.org/package=hunspell.
- [48] Pennington, J., Socher, R. and Manning, C.D. (2014). GloVe: Global vectors for word representation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP-14), Doha, Qatar, pp. 1532–1543.
- [49] Platt, J.C. (1998). Fast training of support vector machines using sequential minimal optimization, in B. Schölkopf et al. (Eds.), Advances in Kernel Methods: Support Vector Learning, MIT Press, Cambridge, MA, pp.185–208.
- [50] Platt, J.C. (2000). Probabilistic outputs for support vector machines and comparison to regularized likelihood methods, in A.J. Smola et al. (Eds.), Advances in Large Margin Classifiers, MIT Press, Cambridge, MA, pp. 61–74.
- [51] Quinlan, J.R. (1986). Induction of decision trees, Machine Learning 1: 81–106.
- [52] R Development Core Team (2016). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, http://www.R-project.org.
- [53] Radovanović, M. and Ivanović, M. (2008). Text mining: Approaches and applications, Novi Sad Journal of Mathematics 38(3): 227–234.
- [54] Rios, G. and Zha, H. (2004). Exploring support vector machines and random forests for spam detection, Proceedings of the 1st International Conference on Email and Anti Spam (CEAS-04), Mountain View, CA, USA, pp. 398–403.
- [55] Rousseau, F., Kiagias, E. and Vazirgiannis, M. (2015). Text categorization as a graph classification problem, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics and the 6th International Joint Conference on Natural Language Processing (ACLIJCNLP-15), Beijing, China, pp. 1702–1712.
- [56] Said, D. and Wanas, N. (2011). Clustering posts in online discussion forum threads, International Journal of Computer Science and Information Technology 3(2): 1–14.
- [57] Schölkopf, B. and Smola, A.J. (2001). Learning with Kernels, MIT Press, Cambridge, MA.
- [58] Sebastiani, F. (2002). Machine learning in automated text categorization, ACM Computing Surveys 34(1): 1–47.
- [59] Selivanov, D. (2016). text2vec: Modern Text Mining Framework for R, R package version 0.4.0, https://CRAN.R-project.org/package=text2vec.
- [60] Siwek, K. and Osowski, S. (2016). Data mining methods for prediction of air pollution, International Journal of Applied Mathematics and Computer Science 26(2): 467–478, DOI: 10.1515/amcs-2016-0033.
- [61] Szymański, J. (2014). Comparative analysis of text representation methods using classification, Cybernetics and Systems 45(2): 180–199.
- [62] Wu, Q., Ye, Y., Zhang, H., Ng, M.K. and Ho, S.-H. (2014). ForesTexter: An efficient random forest algorithm for imbalanced text categorization, Knowledge-Based Systems 67: 105–116.
- [63] Xu, B., Guo, X., Ye, Y. and Cheng, J. (2012). An improved random forest classifier for text categorization, Journal of Computers 7(12): 2913–2920.
- [64] Xue, D. and Li, F. (2015). Research of text categorization model based on random forests, 2015 IEEE International Conference on Computational Intelligence and Communication Technology (CICT-15), Ghaziabad, India, pp. 173–176.
- [65] Yang, Y. and Pedersen, J. (1997). A comparative study on feature selection in text categorization, Proceedings of the 14th International Conference on Machine Learning (ICML-97), Nashville, TN, USA, pp. 412–420.
- [66] Yessenalina, A. and Cardie, C. (2011). Compositional matrix-space models for sentiment analysis, Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP-11), Edinburgh, UK, pp. 172–182.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d20e2cbb-0346-4a8c-9185-f4318377f9fe