Abstract
In this paper, we introduce a novel approach named TopicsRanksDC for topics ranking based on the distance between two clusters that are generated by each topic. We assume that our data consists of text documents that are associated with two-classes. Our approach ranks each topic contained in these text documents by its significance for separating the two-classes. Firstly, the algorithm detects topics using Latent Dirichlet Allocation (LDA). The words defining each topic are represented as two clusters, where each one is associated with one of the classes. We compute four distance metrics, Single Linkage, Complete Linkage, Average Linkage and distance between the centroid. We compare the results of LDA topics and random topics. The results show that the rank for LDA topics is much higher than random topics. The results of TopicsRanksDC tool are promising for future work to enable search engines to suggest related topics.
This work has been partially supported by the “Wachstumskern Qurator – Corporate Smart Insights” project (03WKDA1F) funded by the German Federal Ministry of Education and Research (BMBF).
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Notes
- 1.
Joachims, T.: A Statistical Learning Model of Text Classification with Support Vector Machines. In: Proceedings of the Conference on Research and Development in Information Retrieval, SIGIR (2001).
- 2.
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006).
- 3.
References
Al Qundus, J., Peikert, S., Paschke, A.: AI supported topic modeling using KNIME-workflows. In: Conference on Digit Curation Technologies, Berlin, Germany (2020)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Wei, L., McCallum, A.: Pachinko: allocation DAG-structured mixture models of topic correlations. In: ACM International Conference Proceeding Series (2006)
Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1999 (1999)
Allahyari, M., Kochut, K.: Automatic topic labeling using ontology-based topic models. In: Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 (2016)
Hulpus, I., Hayes, C., Karnstedt, M., Greene, D.: Unsupervised graph-based topic labelling using DBpedia. In: WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search Data Mining (2013)
AlSumait, L., Barbará, D., Gentle, J., Domeniconi, C.: Topic significance ranking of LDA generative models. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS (LNAI), vol. 5781, pp. 67–82. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04180-8_22
Song, Y., Pan, S., Liu, S., Zhou, M.X., Qian, W.: Topic and keyword re-ranking for LDA-based topic modeling. In: International Conference on Information and Knowledge Management Proceedings (2009)
Wang, X., McCallum, A.: Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2006)
Mehta, V., Caceres, R.S., Carter, K.M.: Evaluating topic quality using model clustering. In: IEEE SSCI 2014–2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings (2015)
Al Qundus, J., Paschke, A., Kumar, S., Gupta, S.: Calculating trust in domain analysis: theoretical trust model. Int. J. Inf. Manage. 48, 1–11 (2019)
Qundus, J.A., Paschke, A.: Investigating the effect of attributes on user trust in social media. In: Elloumi, M., Granitzer, M., Hameurlain, A., Seifert, C., Stein, B., Tjoa, A.M., Wagner, R. (eds.) DEXA 2018. CCIS, vol. 903, pp. 278–288. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99133-7_23
Al Qundus, J., Paschke, A., Gupta, S., Alzouby, A., Yousef, M.: Exploring the impact of short text complexity and structure on its quality in social media. J. Enterp. Inf. Manage. (2020)
Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kötter, T., Meinl, T., et al.: KNIME: the Konstanz information miner. SIGKDD Explor. 319–326 (2008)
Xu, Q.-S., Liang, Y.-Z.: Monte Carlo cross validation. Chemom. Intell. Lab. Syst. 56, 1–11 (2001)
Manevitz, L., Yousef, M.: One-class document classification via Neural Networks. Neurocomputing 70, 1466–81 (2007)
Manevitz, L.M., Yousef, M.: One-class SVMs for document classification. J. Mach. Learn. Res. 2, 139–154 (2001)
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Yousef, M., Qundus, J.A., Peikert, S., Paschke, A. (2020). TopicsRanksDC: Distance-Based Topic Ranking Applied on Two-Class Data. In: Kotsis, G., et al. Database and Expert Systems Applications. DEXA 2020. Communications in Computer and Information Science, vol 1285. Springer, Cham. https://doi.org/10.1007/978-3-030-59028-4_2
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