Graph vs. bag representation models for the topic classification of web documents

Abstract

Text classification constitutes a popular task in Web research with various applications that range from spam filtering to sentiment analysis. In this paper, we argue that its performance depends on the quality of Web documents, which varies significantly. For example, the curated content of news articles involves different challenges than the user-generated content of blog posts and Social Media messages. We experimentally verify our claim, quantifying the main factors that affect the performance of text classification. We also argue that the established bag-of-words representation models are inadequate for handling all document types, as they merely extract frequent, yet distinguishing terms from the textual content of the training set. Thus, they suffer from low robustness in the context of noisy or unseen content, unless they are enriched with contextual, application-specific information. In their place, we propose the use of n-gram graphs, a model that goes beyond the bag-of-words representation, transforming every document into a graph: its nodes correspond to character or word n-grams and the co-occurring ones are connected by weighted edges. Individual document graphs can be combined into class graphs and graph similarities are employed to position and classify documents into the vector space. This approach offers two advantages with respect to bag models: first, classification accuracy increases due to the contextual information that is encapsulated in the edges of the n-gram graphs. Second, it reduces the search space to a limited set of robust, endogenous features that depend on the number of classes, rather than the size of the vocabulary. Our thorough experimental study over three large, real-world corpora confirms the superior performance of n-gram graphs across the main types of Web documents.

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Notes

  1. 1.

    http://wordnet.princeton.edu

  2. 2.

    https://twitter.com

  3. 3.

    An alternative approach to forming a class vector is to extract the centroid from the vectors of the individual documents it comprises [29].

  4. 4.

    Example borrowed from [29].

  5. 5.

    The implementation of this procedure in Java is provided publicly through the “Text Representation Models” project of Sourceforge.net at: http://sourceforge.net/projects/textmodels.

  6. 6.

    It is worth stressing that these three types do not correspond to document genres; instead, the aim is to explain the difference in the quality of Web documents and the resulting impact on TC.

  7. 7.

    http://www.facebook.com

  8. 8.

    http://www.youtube.com

  9. 9.

    http://sourceforge.net/projects/jinsect

  10. 10.

    http://www.cs.waikato.ac.nz/ml/weka

  11. 11.

    http://trec.nist.gov/data/reuters/reuters.html

  12. 12.

    http://www.blogpulse.com/www2006-workshop/datashare-instructions.txt

  13. 13.

    A hashtag in Twitter consists of the symbol #, followed by a series of concatenated words and/or alphanumerics (e.g., #worldcup2014).

  14. 14.

    The nominal features are also useful for powerful classification algorithms that are inherently crafted for this kind of evidence, such as C4.5. However, preliminary experiments demonstrated that such algorithms do not scale well to the large search space of bag models. Hence, we do not consider them in our analysis.

  15. 15.

    A “dependency triple” is a language-dependent feature comprising two words that are semantically connected with one of the syntactic relators that are supported by the corresponding parser. For example, s u b j(Y,X) denotes a feature consisting of a noun Y that is connected with a verb X through the relator “subject”.

  16. 16.

    Topic Detection is similar to Topic Classification, but differs in that it involves many more classes, which are also so rare that an unlabelled document is likely to belong to none of them [37].

References

  1. 1.

    Amini, M.R., Usunier, N., Goutte, C.: Learning from multiple partially observed views - an application to multilingual text categorization. In: NIPS, pp. 28–36 (2009)

  2. 2.

    Batista, F., Ribeiro, R.: Sentiment analysis and topic classification based on binary maximum entropy classifiers. Proc. Leng. Nat. 50, 77–84 (2013)

  3. 3.

    Berry, M.W., Kogan, J.: Text Mining: Applications and Theory. Wiley, Chichester (2010)

  4. 4.

    Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

  5. 5.

    Choudhary, B., Bhattacharyya, P.: Text clustering using semantics. World Wide Web Conference (2002)

  6. 6.

    Choudhary, B., Bhattacharyya, P.: Text clustering using universal networking language representation. World Wide Web Conference (2002)

  7. 7.

    Dasgupta, A., Drineas, P., Harb, B., Josifovski, V., Mahoney, M.W.: Feature selection methods for text classification. In: KDD, pp 230–239 (2007)

  8. 8.

    Deerwester, S., Dumais, S., Furnas, G., Landauer, T., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)

    Article  Google Scholar 

  9. 9.

    D’hondt, E., Verberne, S., Koster, C.H.A., Boves, L.: Text representations for patent classification. Comput. Linguist. 39(3), 755–775 (2013)

    Article  Google Scholar 

  10. 10.

    Dumais, S., Chen, H.: Hierarchical classification of web content. In: SIGIR, pp. 256–263 (2000)

  11. 11.

    Eisenstein, J., O’Connor, B., Smith, N.A., Xing, E.P.: A latent variable model for geographic lexical variation. In: EMNLP, pp. 1277–1287 (2010)

  12. 12.

    Fan, R., Chang, K., Hsieh, C., Wang, X., Lin, C.: Liblinear: A library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)

    MATH  Google Scholar 

  13. 13.

    Figueiredo, F., Belém, F., Pinto, H., Almeida, J.M., Gonçalves, M.A., Fernandes, D., de Moura, E.S., Cristo, M.: Evidence of quality of textual features on the web 2.0. In: CIKM, pp 909–918 (2009)

  14. 14.

    Forman, G.: An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3, 1289–1305 (2003)

    MATH  Google Scholar 

  15. 15.

    Garcia Esparza, S., O’Mahony, M., Smyth, B.: Towards tagging and categorization for micro-blogs. In: AICS (2010)

  16. 16.

    Genc, Y., Sakamoto, Y., Nickerson, J.V.: Discovering Context: Classifying Tweets through a Semantic Transform Based on Wikipedia, pp 484–492 (2011)

  17. 17.

    Giannakopoulos, G., Karkaletsis, V., Vouros, G.A., Stamatopoulos, P.: Summarization system evaluation revisited: N-gram graphs. TSLP 5(3) (2008)

  18. 18.

    Giannakopoulos, G., Palpanas, T.: Content and type as orthogonal modeling features: a study on user interest awareness in entity subscription services. Int. J. Adv. Netw. Serv. 3(2) (2010)

  19. 19.

    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data mining software: An update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)

    Article  Google Scholar 

  20. 20.

    Hong, L., Davison, B.: Empirical study of topic modeling in twitter. In: SOMA, pp. 80–88 (2010)

  21. 21.

    Irani, D., Webb, S., Pu, C., Li, K.: Study of trend-stuffing on twitter through text classification. In: CEAS, pp. 40–49 (2010)

  22. 22.

    Joachims, T.: Text categorization with suport vector machines: Learning with many relevant features. In: ECML, pp. 137–142 (1998)

  23. 23.

    Keselj, V., Peng, F., Cercone, N., Thomas, C.: N-gram-based author profiles for authorship attribution. In: PACLING, pp. 255–264 (2003)

  24. 24.

    Khorsi, A.: An overview of content-based spam filtering techniques. Informatica 31, 269–277 (2007)

    MATH  Google Scholar 

  25. 25.

    Kinsella, S., Passant, A., Breslin, J.G.: Topic classification in social media using metadata from hyperlinked objects. In: ECIR, pp 201–206 (2011)

  26. 26.

    Kinsella, S., Wang, M., Breslin, J.G., Hayes, C.: Improving categorisation in social media using hyperlinks to structured data sources. In: ESWC (2), pp 390–404 (2011)

  27. 27.

    Li, Z., Zhou, D., Juan, Y.F., Han, J.: Keyword extraction for social snippets. In: WWW, pp. 1143–1144 (2010)

  28. 28.

    Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.: Text classification using string kernels. J. Mach. Learn. Res. 2, 419–444 (2002)

    MATH  Google Scholar 

  29. 29.

    Manning, C., Raghavan, P., Schuetze, H.: Introduction to information retrieval, vol. 1. Cambridge University Press (2008)

  30. 30.

    Meng, W., Lanfen, L., Jing, W., Penghua, Y., Jiaolong, L., Fei, X.: Improving short text classification using public search engines. In: Integrated Uncertainty in Knowledge Modelling and Decision Making, pp 157–166 (2013)

  31. 31.

    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining, pp. 1320–1326. LREC (2010)

  32. 32.

    Peng, F., Schuurmans, D.: Combining naive bayes and n-gram language models for text classification. Advances in Information Retrieval, pp. 547–547 (2003)

  33. 33.

    Phan, X.H., Nguyen, M.L., Horiguchi, S.: Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In: WWW, pp. 91–100 (2008)

  34. 34.

    Rosa, H., Batista, F., Carvalho, J.P.: Twitter topic fuzzy fingerprints. In: IEEE International Conference on Fuzzy Systems, pp 776–783 (2014)

  35. 35.

    Salton, G.: The Smart Retrieval System – Experiments in Automatic Document Processing, p. 556. Prentice-Hall (1971)

  36. 36.

    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)

    Article  Google Scholar 

  37. 37.

    Sebastiani, F.: Text categorization. In: Encyclopedia of Database Technologies and Applications, pp. 683–687 (2005)

  38. 38.

    Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., Demirbas, M.: Short text classification in twitter to improve information filtering. In: SIGIR, pp. 841–842 (2010)

  39. 39.

    Stamatatos, E.: Ensemble-based author identification using character n-grams. In: Proceedings of the 3rd International Workshop on Text-based Information Retrieval, pp. 41–46 (2006)

  40. 40.

    Stamatatos, E.: A survey of modern authorship attribution methods. J. Am. Soc. Inf. Sci. Technol. 60(3), 538–556 (2009)

    Article  Google Scholar 

  41. 41.

    Stamatatos, E.: On the robustness of authorship attribution based on character n-gram features. J. Law Policy 21(2), 421–439 (2013)

    Google Scholar 

  42. 42.

    Stamatatos, E., Fakotakis, N., Kokkinakis, G.: Automatic text categorization in terms of genre and author. Comput. Linguist. 26(4), 471–495 (2000)

    Article  Google Scholar 

  43. 43.

    Sun, X., Wang, H., Yu, Y.: Towards effective short text deep classification. In: SIGIR, pp. 1143–1144 (2011)

  44. 44.

    Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, p. 560. Morgan Kaufmann, San Francisco (2005)

  45. 45.

    Yang, J., Leskovec, J.: Patterns of temporal variation in online media. In: WSDM, pp. 177–186 (2011)

  46. 46.

    Yang, S., Kolcz, A., Schlaikjer, A., Gupta, P.: Large-scale high-precision topic modeling on twitter. In: KDD, pp. 1907–1916 (2014)

  47. 47.

    Zelikovitz, S., Hirsh, H.: Transductive lsi for short text classification problems. In: FLAIRS, pp. 556–561 (2004)

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Papadakis, G., Giannakopoulos, G. & Paliouras, G. Graph vs. bag representation models for the topic classification of web documents. World Wide Web 19, 887–920 (2016). https://doi.org/10.1007/s11280-015-0365-x

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Keywords

  • Text classification
  • N-gram graphs
  • Web document types