WalkToTopics: Inferring Topic Relations from a Feature Learning Perspective

  • Linan Gao
  • Zeyu WangEmail author
  • Shanqing Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)


The increasing number of documents is leading to more and more topics nowadays. Understanding the relations between different topics evolved in documents become more important and challenging for users. Although many topic models have been devoted to analyzing topics, the study of topics’ potential relevances is still largely limited by various difficulties. Hence, we introduce WalkToTopics, an unsupervised topic mining and analysis model, for inferring potential relevances between different topics. Relying on an advanced feature learning technique to automatically summarize topic’s neighborhood features, WalkToTopics can reveal latent relations between different topics. Compared to existing approaches, our model is able to predict the relationship between any two individual topics of documents, and it does not require any prior knowledge of the existing topics’ relations and dictionaries. Moreover, WalkToTopics is a general model that also can work on exploring topic clusters or extracting sentiments, and can be applied to potential applications, such as ideas tracking and opinion summarization. Finally, we conducted two studies for common users and experts which both quantitatively and qualitatively demonstrate the effectiveness of WalkToTopics in helping users’ understanding of hidden relevances between topics on social media.


Topic relations Feature learning Random walk Relevance prediction 



We thank all the anonymous reviewers for their insightful comments. This work is partially supported by National Natural Science Foundation of China (91546203), the Key Science Technology Project of Shandong Province (2015GGX101046), the Shandong Provincial Natural Science Foundation (ZR2014FM020), Major Scientific and Technological Innovation Projects of Shandong Province, China (No. 2017CXGC0704) and Fundamental Research Fund of Shandong Academy of Sciences (No. 2018:12-16).


  1. 1.
    Blair, S.J., Bi, Y., Mulvenna, M.D.: Increasing topic coherence by aggregating topic models. In: Lehner, F., Fteimi, N. (eds.) KSEM 2016. LNCS, vol. 9983, pp. 69–81. Springer, Cham (2016). Scholar
  2. 2.
    Blei, D.M., Lafferty, J.D.: Dynamic topic models. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 113–120. ACM (2006)Google Scholar
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)zbMATHGoogle Scholar
  4. 4.
    Cumming, G.: Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge, Abingdon (2013)Google Scholar
  5. 5.
    Hu, Y., Xu, X., Li, L.: Analyzing topic-sentiment and topic evolution over time from social media. In: Lehner, F., Fteimi, N. (eds.) KSEM 2016. LNCS, vol. 9983, pp. 97–109. Springer, Cham (2016). Scholar
  6. 6.
    Lin, C., He, Y., Everson, R., Ruger, S.: Weakly supervised joint sentiment-topic detection from text. IEEE Trans. Knowl. Data Eng. 24(6), 1134–1145 (2012)CrossRefGoogle Scholar
  7. 7.
    Mcauliffe, J.D., Blei, D.M.: Supervised topic models. In: Advances in Neural Information Processing Systems, pp. 121–128 (2008)Google Scholar
  8. 8.
    Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.: Topic sentiment mixture: modeling facets and opinions in weblogs. In: Proceedings of the 16th international conference on World Wide Web, pp. 171–180. ACM (2007)Google Scholar
  9. 9.
    Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)CrossRefGoogle Scholar
  10. 10.
    Noack, A.: Modularity clustering is force-directed layout. Phys. Rev. E 79(2), 026102 (2009)CrossRefGoogle Scholar
  11. 11.
    Porter, M.: An algorithm for suffix stripping. Program 40(3), 211–218 (1980)CrossRefGoogle Scholar
  12. 12.
    Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York (1983)zbMATHGoogle Scholar
  13. 13.
    Walker, A.J.: An efficient method for generating discrete random variables with general distributions. ACM Trans. Math. Softw. 3(3), 253–256 (1977)CrossRefGoogle Scholar
  14. 14.
    Wang, X., McCallum, A.: Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 424–433. ACM (2006)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of SoftwareShandong UniversityJinanChina
  2. 2.Taishan CollegeShandong UniversityJinanChina
  3. 3.Key Laboratory of Cryptologic Technology and Information Security, Ministry of EducationShandong UniversityJinanChina

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