1,615 research outputs found
Positive words carry less information than negative words
We show that the frequency of word use is not only determined by the word
length \cite{Zipf1935} and the average information content
\cite{Piantadosi2011}, but also by its emotional content. We have analyzed
three established lexica of affective word usage in English, German, and
Spanish, to verify that these lexica have a neutral, unbiased, emotional
content. Taking into account the frequency of word usage, we find that words
with a positive emotional content are more frequently used. This lends support
to Pollyanna hypothesis \cite{Boucher1969} that there should be a positive bias
in human expression. We also find that negative words contain more information
than positive words, as the informativeness of a word increases uniformly with
its valence decrease. Our findings support earlier conjectures about (i) the
relation between word frequency and information content, and (ii) the impact of
positive emotions on communication and social links.Comment: 16 pages, 3 figures, 3 table
A network model of interpersonal alignment in dialog
In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutorsâ dialog lexica. This is done by means of so-called two-layer time-aligned network series, that is, a time-adjusted graph model. The graph model is partitioned into two layers, so that the interlocutorsâ lexica are captured as subgraphs of an encompassing dialog graph. Each constituent network of the series is updated utterance-wise. Thus, both the inherent bipartition of dyadic conversations and their gradual development are modeled. The notion of alignment is then operationalized within a quantitative model of structure formation based on the mutual information of the subgraphs that represent the interlocutorâs dialog lexica. By adapting and further developing several models of complex network theory, we show that dialog lexica evolve as a novel class of graphs that have not been considered before in the area of complex (linguistic) networks. Additionally, we show that our framework allows for classifying dialogs according to their alignment status. To the best of our knowledge, this is the first approach to measuring alignment in communication that explores the similarities of graph-like cognitive representations. Keywords: alignment in communication; structural coupling; linguistic networks; graph distance measures; mutual information of graphs; quantitative network analysi
Reconstructing phonologies of dead languages: the case of Late Greek <H>
This article compares prescriptive texts of the Indian and of the Greek scholarly tradition (PratiÂźakhya and Atticist lexica), with a focus on a specific problem of Late Greek
phonology, the pronunciation of âčĂâș. The Greek case-study shows how the learned
texts attest to a conservative language of the educated. This variety retained special
sets of phonological traits, perhaps for much a longer period than the same traits had
survived in non-educated variants: the lexica that attest to it provide therefore valuable
evidence of language change in Late Greek
Stabilizing knowledge through standards - A perspective for the humanities
It is usual to consider that standards generate mixed feelings among
scientists. They are often seen as not really reflecting the state of the art
in a given domain and a hindrance to scientific creativity. Still, scientists
should theoretically be at the best place to bring their expertise into
standard developments, being even more neutral on issues that may typically be
related to competing industrial interests. Even if it could be thought of as
even more complex to think about developping standards in the humanities, we
will show how this can be made feasible through the experience gained both
within the Text Encoding Initiative consortium and the International
Organisation for Standardisation. By taking the specific case of lexical
resources, we will try to show how this brings about new ideas for designing
future research infrastructures in the human and social sciences
Developing conceptual glossaries for the Latin vulgate bible.
A conceptual glossary is a textual reference work that combines the features of a thesaurus and an index verborum. In it, the word occurrences within a given text are classified, disambiguated, and indexed according to their membership of a set of conceptual (i.e. semantic) fields. Since 1994, we have been working towards building a set of conceptual glossaries for the Latin Vulgate Bible. So far, we have published a conceptual glossary to the Gospel according to John and are at present completing the analysis of the Gospel according to Mark and the minor epistles. This paper describes the background to our project and outlines the steps by which the glossaries are developed within a relational database framework
A review of sentiment analysis research in Arabic language
Sentiment analysis is a task of natural language processing which has
recently attracted increasing attention. However, sentiment analysis research
has mainly been carried out for the English language. Although Arabic is
ramping up as one of the most used languages on the Internet, only a few
studies have focused on Arabic sentiment analysis so far. In this paper, we
carry out an in-depth qualitative study of the most important research works in
this context by presenting limits and strengths of existing approaches. In
particular, we survey both approaches that leverage machine translation or
transfer learning to adapt English resources to Arabic and approaches that stem
directly from the Arabic language
Building a Call to Action: Social Action in Networks of Practice
The three research papers completed as part of this dissertation explore how people contributing to #BlackLivesMatter build knowledge, using social construction of knowledge (SCK), and what they are building knowledge about, using critical consciousness, because understanding how these processes play out on Twitter provides a way for others to understand this social movement. Paper 1 describes a new methodological approach to combining social network analysis (SNA) and social learning analytics to assess SCK. The sequential mixed method design begins by conducting a content analysis according to the Interaction Analysis Model (IAM). The results of the content analysis yield descriptive data that can be used to conduct SNA and social learning analytics.
The purpose of Paper 2 was to use the typology of digital activism actions identified by Penney and Dadas (2014) from interviews with digital activists to validate them in a quantitative study. Paper 2 found that the actions taken by people who are helping to facilitate face-to-face action (p \u3c .0000001 , r = -0.076) or provide face-to-face updates (p \u3c .0000001 , r = -0.060) were negatively correlated with the actions of people who were facilitating online actions suggesting that digital activists should be treated as a unique population of activists.
Paper 3 used the outcomes of a content analysis and lexicon analysis performed on #BlackLivesMatter data to determine 1) the levels of SCK and critical consciousness present in online data and 2) social learning analytics to ascertain the extent that SCK and critical consciousness can predict social action. Results of the content analysis and lexicon analysis found all levels of SCK and critical consciousness in the data. Results of social learning analytics conducted using NaĂŻve Bayes classification indicate that SCK and critical consciousness can only predict information sharing behaviors of online social action like personal opinions, forwarding information, and engaging in discussion. Evidence of information sharing behaviors on Twitter provides a high degree of confidence that further research including replies and other interactions between users will reveal robust SCK
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