From Sentiment to Reputation: ILPS at RepLab 2012

We report on our participation in the profiling task of the first edition of the CLEF RepLab evaluation initiative. We assume that a statement - such as a tweet - that carries negative sentiment can have a positive impact on the reputation of the entity it talks about (and vice versa). Our model directly captures this impact by observing the reactions - such as replies - the statement solicits. We present the assumptions behind our model and the model itself. We find that given the current setting, results on the test set are strongly entity-dependent and that the test data is very different from the trial data. We conclude with a proposal on how to create a task that avoids such dataset dependent problems.