Detecting the Reputation Polarity of Microblog Posts

We address the task of detecting the reputation polarity of social media updates, that is, deciding whether the content of an update has positive or negative implications for the reputation of a given entity. Typical approaches to this task include sentiment lexicons and linguistic features. However, they fall short in the social media domain because of its unedited and noisy nature, and, more importantly, because reputation polarity is not only encoded in sentiment-bearing words but it is also embedded in other word usage. To this end, automatic methods for extracting discriminative features for reputation polarity detection can play a role. We propose a data-driven, supervised approach for extracting textual features, which we use to train a reputation polarity classifier. Experiments on the RepLab 2013 collection show that our model outperforms the state-of-the-art method based on sentiment analysis by 20% accuracy.