Modelling Techniques for Twitter Contents: A Step beyond Classification based Approaches

In this paper we present our first participation at RepLab Campaign. Our work is focused in two contributions. The first one is the use of an IR method to address Polarity and Filtering tasks. These two tasks can be seen as the same problem: to find the most relevant class to annotate a given tweet. For that, we applied a classical IR approach, using the tweet content as query against an index with the models of the classes used to annotate tweets. To model these classes we propose the use of the Kullback Leibler Divergence (KLD), in order to extract their most representative terminology. Different data and ways to model these data (through KLD) are also proposed. The second contribution is related to the Topic Detection task. Instead a clustering based technique; we propose the application of Formal Concept Analysis (FCA) to represent the contents in a lattice structure. To extract topics from the lattice, we applied a FCA concept: stability. According to the results, our IR based approach has been proven as very satisfactory for the Polarity task, while for the Filtering task, it seems to be less suitable. On the other hand FCA modelling has been demonstrated as a promising methodology for Topic Detection, achieving high successful results.