Sentiment lexica are useful for analyzing opinions in Web collections, for domain-dependent sentiment classification, and as sub-components of recommender systems. In this paper, we present a strategy for automatically generating topic-dependent lexica from large corpora of review articles by exploiting accompanying user ratings. Our approach combines text segmentation, discriminative feature analysis techniques, and latent topic extraction to infer the polarity of n-grams in a topical context. Our experiments on rating prediction demonstrate a substantial performance improvement in comparison with existing state-of-the-art sentiment lexica.
Watch our new MOOC in German about hate and fake in the Internet ("Trolle, Hass und Fake-News: Wie können wir das Internet retten?") on openHPI (link).
Our work on Measuring and Comparing Dimensionality Reduction Algorithms for Robust Visualisation of Dynamic Text Collections will be presented at CHIIR 2021.
I added some photos from my trip to Hildesheim.