Publication Type:

Journal Article

Source:

Journal of Advanced Research in Dynamical and Control Systems, Institute of Advanced Scientific Research, Inc., Volume 2017, Number Special Issue 11, p.175-183 (2017)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030643580&partnerID=40&md5=bcb2c4cf15ed792716c981885b0343b8

Abstract:

Topic Modeling is a statistical model, which derives the latent theme from large collection of text. In this work we developed a topic model for BBC news corpus to find the screened regional from the corpus. We have implemented the topic modeling algorithms Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA) and three different machine learning approaches (Naive Bayes, K-NN and K-means). We compared the performance of topic modeling algorithms with machine learning approaches using the measures precision and recall. Our results show that topic modeling algorithms work better for corpus with multiple topic distribution.

Notes:

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Cite this Research Publication

T. Rajasundari, Subathra P., and Kumar, P. N., “Performance Analysis of Topic Modeling Algorithms for News Articles”, Journal of Advanced Research in Dynamical and Control Systems, vol. 2017, pp. 175-183, 2017.