Learn More
A major limitation in the automatic detection of affect, feelings, emotions, sentiments, and opinions in text is the lack of proper differentiation between these subjective terms and understanding of how they relate to one another. This lack of differentiation not only leads to inconsistency in terminology usage but also makes the subtleties and nuances(More)
Learning diaries are instruments through which students can reflect on their learning experience. Students' sentiments, emotions, opinions and attitudes are embedded in their learning diaries as part of the process of understanding their progress during the course and the self-awareness of their goals. Learning diaries are also a very informative feedback(More)
We report on experiments that demonstrate the relevance of our AntiSocial Behavior (ASB) corpus as a machine learning resource to detect antisocial behavior from text. We first describe the corpus and then, by using the corpus for training machine learning algorithms, we build a set of binary classifiers. Experimental evaluations revealed that classifiers(More)
This article investigates a blended project-based approach that was introduced to forestry and ICT undergraduates as an extracurricular activity at the University of Eldoret, Kenya. The approach blends problem-based learning and participatory design to solve real-life forestry problems. Even though the use of the approach itself is not novel, in this(More)
A considerable amount of effort has been made to reduce the physical manifestation of antisocial behaviour (ASB) in communities. However, the key to the early detection of ASB is, in many cases, in observing its manifestations in written language, which has not been studied in detail. In this work, we search for linguistic features that pertain to ASB in(More)