Fecha: 18 de Junio de 2013

Ponente: Maarten de Rijke (Universiteit van Amsterdam)

Lugar de celebración: Salón de Actos, Facultad de Psicología, UNED


In reputation management, knowing the impact a tweet has on the reputation of a brand or company is crucial. The reputation polarity of a tweet is a measure for how a tweet influences the reputation of a brand or company. We consider the task of determining the reputation polarity of a tweet. For this classification task, I propose a feature-based model based on three dimensions: the source of the tweet, the contents of the tweet and the reception of the tweet, i.e., how the tweet is being perceived. For evaluation purposes, I make use of the recently introduced RepLab data set. I contrast two training paradigms. The first is independent of the entity whose reputation is being determined, the second depends on the entity at stake, but which, on average, has over 90% fewer training samples per model. I will show that having less but entity-dependent training data is significantly more effective for predicting the reputation polarity of a tweet. The relative effectiveness of features is shown to depend on the training paradigm used.

(This is based on joint work with Hendrike Peetz)