Identifying Promising Research Topics in Computer Science
In this paper, we investigate an interpretable definition of promising research topics, complemented with a predictive model. Two methods of topic identification were employed: bag of words and the LDA model, with reflection on their applicability and usefulness in the task of retrieving topics on a set of publication titles. Next, different criteria for promising topic were analyzed with respect to their usefulness and shortcomings. For verification purposes, the DBLP data set, an online open reference of computer science publications, is used. The presented results reveal potential of the proposed method for identification of promising research topics.
KeywordsResearch prediction Promising topic Topic modelling DBLP
This work was partially supported by the National Science Centre, Poland, project no. 2016/21/B/ST6/01463 and by European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant no. 691152 (RENOIR); by the Polish Ministry of Science and Higher Education fund for supporting internationally co-financed projects in 2016-2019, no. 3628/H2020/2016/2.
- 1.Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Adv. Neural Inf. Proces. Syst. 1, 601–608 (2002)Google Scholar
- 3.He, Q., Chen, B., Pei, J., Qiu, B., Mitra, P., Giles, L.: Detecting topic evolution in scientific literature: how can citations help? In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM ’09, pp. 957–966 (2009)Google Scholar
- 4.Hong, L., Davison, B.D.: Empirical study of topic modeling in twitter. In: Proceedings of the First Workshop on Social Media Analytics, SOMA ’10, pp. 80–88 (2010)Google Scholar
- 6.Loper, E., Bird, S.: Nltk: The natural language toolkit. In: Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics - Volume 1, ETMTNLP ’02, pp. 63–70 (2002)Google Scholar
- 9.Prabhakaran, V., Hamilton, W.L., McFarland, D.A., Jurafsky, D.: Predicting the rise and fall of scientific topics from trends in their rhetorical framing. In: ACL (2016)Google Scholar
- 10.Wang, Y., Joo, S., Lu, K.: Exploring topics in the field of data science by analyzing Wikipedia documents: a preliminary result. Proc. Am. Soc. Inform. Sci. Technol. 51(1), 1–4 (2014)Google Scholar