Affiliations: [a] Social Media Research Group, Institute of Technology Tallaght, Dublin, Ireland | [b] NLE Lab. - PRHLT Research Center, Universitat Politècnica de València, Spain | [c] FCC, Benemérita Universidad Autónoma de Puebla, Puebla, Mexico
Correspondence:
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Corresponding author: Fernando Perez-Tellez, SMRG - Institute of Technology Tallaght Dublin, Tallaght, Dublin 24, Ireland. E-mail:[email protected]
Abstract: In the last 10 years, the information generated on weblog
sites has increased exponentially, resulting in a clear need for intelligent
approaches to analyse and organise this massive amount of information. In
this work, we present a methodology to cluster weblog posts according to the
topics discussed therein, which we derive by text analysis. We have called
the methodology Prototype/Topic Based Clustering, an approach which is based on a generative probabilistic
model in conjunction with a Self-Term Expansion methodology. The usage of
the Self-Term Expansion methodology is to improve the representation of the
data and the generative probabilistic model is employed to identify relevant
topics discussed in the weblogs. We have modified the generative
probabilistic model in order to exploit predefined initialisations of the
model and have performed our experiments in narrow and wide domain subsets.
The results of our approach have demonstrated a considerable improvement
over the pre-defined baseline and alternative state of the art approaches,
achieving an improvement of up to 20% in many cases. The experiments were
performed on both narrow and wide domain datasets, with the latter showing
better improvement. However in both cases, our results outperformed the
baseline and state of the art algorithms.
Keywords: Short text analysis, weblog clustering, topic Identification
