Michael Gamon

TL;DR: It is found that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement.

TL;DR: A model is proposed that captures the compositional structure of textual relations, and jointly optimizes entity, knowledge base, and textual relation representations, and significantly improves performance over a model that does not share parameters among textual relations with common sub-structure.

TL;DR: It is demonstrated that it is possible to perform automatic sentiment classification in the very noisy domain of customer feedback data by using large feature vectors in combination with feature reduction and the addition of deep linguistic analysis features to a set of surface level word n-gram features contributes consistently to classification accuracy.

TL;DR: A simple but effective technique for clustering sentences, the application of a bootstrapping approach to sentiment classification, and a novel user-interface are described that enables the exploration of large quantities of customer free text.

TL;DR: Four different approaches to customizing a sentiment classification system to a new target domain in the absence of large amounts of labeled data are surveyed and their advantages, disadvantages and performance are discussed.