Using geometric data analysis, our objective is the analysis of narrative, with narrative of emotion being the focus in this work. The following two principles for analysis of emotion inform our work. Firstly, emotion is revealed not as a quality in its own right but rather through interaction. We study the 2-way relationship of Ilsa and Rick in the movie Casablanca, and the 3-way relationship of Emma, Charles and Rodolphe in the novel {\em Madame Bovary}. Secondly, emotion, that is expression of states of mind of subjects, is formed and evolves within the narrative that expresses external events and (personal, social, physical) context. In addition to the analysis methodology with key aspects that are innovative, the input data used is crucial. We use, firstly, dialogue, and secondly, broad and general description that incorporates dialogue. In a follow-on study, we apply our unsupervised narrative mapping to data streams with very low emotional expression. We map the narrative of Twitter streams. Thus we demonstrate map analysis of general narratives.
How are people linked in a highly connected society? Since in many networks a power-law (scale-free) node-degree distribution can be observed, power-law might be seen as a universal characteristics of networks. But this study of communication in the Flickr social online network reveals that power-law node-degree distributions are restricted to only sparsely connected networks. More densely connected networks, by contrast, show an increasing divergence from power-law. This work shows that this observation is consistent with the classic idea from social sciences that similarity is the driving factor behind communication in social networks. The strong relation between communication strength and node similarity could be confirmed by analyzing the Flickr network. It also is shown that node similarity as a network formation model can reproduce the characteristics of different network densities and hence can be used as a model for describing the topological transition from weakly to strongly connected societies.
The contribution of this article is twofold: the adaptation and application of models of deception from psychology, combined with data-mining techniques, to the text of speeches given by candidates in the 2008 U.S. presidential election; and the observation of both short-term andmedium-term differences in the levels of deception. Rather than considering the effect of deception on voters, deception is used as a lens through which to observe the self-perceptions of candidates and campaigns. The method of analysis is fully automated and requires no human coding, and so can be applied to many other domains in a straightforward way. The authors posit explanations for the observed variation in terms of a dynamic tension between the goals of campaigns at each moment in time, for example gaps between their view of the candidate’s persona and the persona expected for the position; and the difficulties of crafting and sustaining a persona, for example, the cognitive cost and the need for apparent continuity with past actions and perceptions. The changes in the resulting balance provide a new channel by which to understand the drivers of political campaigning, a channel that is hard to manipulate because its markers are created subconsciously.
