This paper describes our participation at RepLab 2014, a competitive evaluation for reputation monitoring on Twitter. The fol- lowing tasks were addressed: (1) categorisation of tweets with respect to standard reputation dimensions and (2) characterisation of Twitter profiles, which includes: (2.1) identifying the type of those profiles, such as journalist or investor, and (2.2) ranking the authors according to their level of influence on this social network. We consider an approach based on the application of natural language processing techniques in order to take into account part-of-speech, syntactic and semantic information. However, each task is addressed independently, since they respond to different requirements. The official results confirm the competitiveness of our approaches, which achieve the 2nd place, tied in practice with the 1st place, at the author ranking task; and 3rd place at the reputation dimensions classification tasks.
['David Vilares', 'Miguel Hermo', 'Miguel Ángel Verdugo Alonso', 'Carlos Gómez-Rodríguez', 'Jesús Vilares']
LyS at CLEF RepLab 2014: Creating the State of the Art in Author Influence Ranking and Reputation Classification on Twitter