B. Desmet

In the current era of online interactions, both positive and negative experiences are abundant on the Web. Expand

Automatic detection of cyberbullying in social media text by modelling posts written by bullies, victims, and bystanders of online bullying. Expand

We propose a multimodular approach to account for the diversity of normalization issues encountered in user-generated content (UGC). Expand

We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset, a large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. Expand

We present a fine-grained scheme for the annotation of polar sentiment in text, that accounts for explicit sentiment (so-called private states), as well as implicit expressions of sentiment (polar facts). Expand

We evaluate two different methodologies to assess the readability of a wide variety of text material: A more traditional setup in which expert readers make readability judgments and a crowdsourcing setup for users who are not necessarily experts. Expand

This paper describes the creation of a fine-grained named entity annotation scheme and corpus for Dutch, and experiments on automatic main type and subtype named entity recognition. Expand

We describe a system that uses genetic algorithms to construct the best ensemble from a set of classifiers, based on the approach proposed in [3]. Expand

We investigate whether the recent advances in natural language processing, and more specifically in sentiment mining, can be used to accurately pinpoint 15 different emotions, which might be indicative of suicidal behavior. Expand