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.
