Uned at replab 2012: Monitoring task

Abstract. This paper describes the UNED participation at RepLab 2012 Monitoring Task. Given an entity and a tweet stream containing the entity's name, the task consists on grouping the tweets in topics and then ranking the identified topics by priority. We tested three different systems to deal with the clustering problem: (i) an agglomerative clustering based on term co-occurrences, (ii) a clustering method that considers 'wikified" tweets, where each tweet is represented with a set of Wikipedia entries that are semantically related to it and (iii) Twitter-LDA, a topic modeling approach that extends LDA considering some of the intrinsic properties of Twitter data. For the ranking problem, we rely on the insight that the priority of a topic depends on the sentiment expressed in the subjective tweets that refer to it. Although none of the proposed systems outperforms the official baseline in average, our systems obtain reasonable high precision results, (i.e. high Reliability scores). The average sentiment of a topic seems to be an useful indicator of priority, that merits further study. Finally, topics with high ratio of unrelated tweets are difficult to group correctly, suggesting the need of an explicit treatment of ambiguity