This paper presents a hierarchical Bayesian model based on latent Dirichlet allocation (LDA), called subjLDA, for sentence-level subjectivity detection, which automatically identifies whether a given sentence expresses opinion or states facts. In contrast to most of the existing methods relying on either labelled corpora for classifier training or linguistic pattern extraction for subjectivity ...

Subjectivity and sentiment analysis focuses on the automatic identification of private states, such as opinions, emotions, sentiments, evaluations, beliefs, and speculations in natural language. While subjectivity classification labels text as either subjective or objective, sentiment classification adds an additional level of granularity, by further classifying subjective text as either positi...

Both psychology and psychiatry are dominated by individualistic accounts of paranoia (and indeed, other forms of distress). As a corrective to these, this paper provides a social account of paranoia grounded in a minimal notion of embodied subjectivity constituted from the interpenetration of feelings, perception and discourse. Paranoia is conceptualised as a mode or tendency within embodied su...

Both psychology and psychiatry are dominated by individualistic accounts of paranoia (and indeed, other forms of distress). As a corrective to these, this paper provides a social account of paranoia grounded in a minimal notion of embodied subjectivity constituted from the interpenetration of feelings, perception and discourse. Paranoia is conceptualised as a mode or tendency within embodied su...

As we saw in Chapter 5, a viable understanding of privacy for the networked information society must consider the complexities of the self-society relation and must confront the assumptions that underlie the informationprocessing imperative—the culturally-determined urge to collect more and more information. At the same time, it must avoid conceiving of either subjectivity or privacy in purely ...

We explore the idea of creating a subjectivity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective sentences from objective sentences. First, we use two bootstrapping algorithms that exploit extraction patterns to learn sets of subjective nouns. Then we train a Naive Bayes classifier ...