2,241 research outputs found
Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game
Interpersonal relations are fickle, with close friendships often dissolving
into enmity. In this work, we explore linguistic cues that presage such
transitions by studying dyadic interactions in an online strategy game where
players form alliances and break those alliances through betrayal. We
characterize friendships that are unlikely to last and examine temporal
patterns that foretell betrayal.
We reveal that subtle signs of imminent betrayal are encoded in the
conversational patterns of the dyad, even if the victim is not aware of the
relationship's fate. In particular, we find that lasting friendships exhibit a
form of balance that manifests itself through language. In contrast, sudden
changes in the balance of certain conversational attributes---such as positive
sentiment, politeness, or focus on future planning---signal impending betrayal.Comment: To appear at ACL 2015. 10pp, 4 fig. Data and other info available at
http://vene.ro/betrayal
Better Document-level Sentiment Analysis from RST Discourse Parsing
Discourse structure is the hidden link between surface features and
document-level properties, such as sentiment polarity. We show that the
discourse analyses produced by Rhetorical Structure Theory (RST) parsers can
improve document-level sentiment analysis, via composition of local information
up the discourse tree. First, we show that reweighting discourse units
according to their position in a dependency representation of the rhetorical
structure can yield substantial improvements on lexicon-based sentiment
analysis. Next, we present a recursive neural network over the RST structure,
which offers significant improvements over classification-based methods.Comment: Published at Empirical Methods in Natural Language Processing (EMNLP
2015
Sentiment and behaviour annotation in a corpus of dialogue summaries
This paper proposes a scheme for sentiment annotation. We show how the task can be made tractable by focusing on one of the many aspects of sentiment: sentiment as it is recorded in behaviour reports of people and their interactions. Together with a number of measures for supporting the reliable application of the scheme, this allows us to obtain sufficient to good agreement scores (in terms of Krippendorf's alpha) on three key dimensions: polarity, evaluated party and type of clause. Evaluation of the scheme is carried out through the annotation of an existing corpus of dialogue summaries (in English and Portuguese) by nine annotators. Our contribution to the field is twofold: (i) a reliable multi-dimensional annotation scheme for sentiment in behaviour reports; and (ii) an annotated corpus that was used for testing the reliability of the scheme and which is made available to the research community
Controlling Linguistic Style Aspects in Neural Language Generation
Most work on neural natural language generation (NNLG) focus on controlling
the content of the generated text. We experiment with controlling several
stylistic aspects of the generated text, in addition to its content. The method
is based on conditioned RNN language model, where the desired content as well
as the stylistic parameters serve as conditioning contexts. We demonstrate the
approach on the movie reviews domain and show that it is successful in
generating coherent sentences corresponding to the required linguistic style
and content
Towards a computational model of social norms
We describe a computational model of social norms based on identifying values
that a certain culture finds desirable such as dignity, generosity and
politeness. The model quantifies these values in the form of Culture-Sanctioned
Social Metrics (CSSMs) and treats social norms as the requirement to maximize
these metrics from the perspective of the self, peers and public. This model
can be used to create realistic social simulations, to explain or predict human
behavior in specific scenarios, or as a component of robots or agents that need
to interact with humans in specific social-cultural settings. We validate the
model by using it to represent a complex deception scenario and showing that it
can yield non-trivial insights such as the explanation of apparently irrational
human behavior
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