Comparison of Clustering Algorithms for the Identification of Topics on Twitter

Marjori N. M. Klinczak; Celso A. A. Kaestner

Autores/as

Marjori N. M. Klinczak Escuela Politécnica Nacional
Celso A. A. Kaestner University of Technology of Paraná

Palabras clave:

text processing, clustering algorithms, NMF algorithm, Twitter topics identification

Resumen

Topic Identification in Social Networks has become an important task when dealing with event detection, particularly when global communities are affected. In order to attack this problem, text processing techniques and machine learning algorithms have been extensively used. In this paper we compare four clustering algorithms – k-means, k-medoids, DBSCAN and NMF (Non-negative Matrix Factorization) – in order to detect topics related to textual messages obtained from Twitter. The algorithms were applied to a database initially composed by tweets having hashtags related to the recent Nepal earthquake as initial context. Obtained results suggest that the NMF clustering algorithm presents superior results, providing simpler clusters that are also easier to interpret.

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Biografía del autor/a

Marjori N. M. Klinczak, Escuela Politécnica Nacional

Jenny Torres es subdecana (e) de la Facultad de Ingeniería en Sistemas de la Escuela Politécnica Nacional (EPN). Obtuvo su doctorado en Informática en la Universidad Pierre y Marie Curie de Francia. En 2009 obtuvo su M.Sc en Ciencias Computacionales en la Universidad Paris-Est Créteil. Antes de ser becaria de la SENESCYT, culminó una maestría en Gerencia de Redes y Telecomunicaciones en la Escuela Politécnica del Ejército y en 2006 se graduó de ingeniera en Sistemas en la EPN.
Su investigación se centra en seguridad informática, gestión de redes, gestión de identidades, redes inalámbricas e infraestructuras abiertas. Fue docente invitada durante seis meses en la Universidad de Paraná, Curitiba, Brasil y forma parte de los equipos de investigación Phare y NR2 en Francia y Brasil respectivamente.

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