Political discourse content analysis: a critical overview of a computerized text analysis program linguistic inquiry and word count (liwc)
Angelika Yanovets,Oksana Smal +1 more
- Vol. 1, pp 139-142
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TLDR
The authors examined and analyzed the linguistic and psychological features of political discourse using a computer-based Linguistic Inquiry and Word Count (LIWC) content analysis program to explore the relationship between political discourse and the personality of politicians.Abstract:
The article examines and analyzes the linguistic and psychological features of political discourse using a computer-based Linguistic Inquiry and Word Count (LIWC) content analysis program to explore the relationship between political discourse and the personality of politicians. As for political discourse, it is perhaps the communicator, the linguistic personality, who plays the most important role in the communication. The linguistic personality of a politician is of particular interest in political discourse content-analysis, since it has the greatest influence on the public consciousness via mass media. Using text as a source of psychological and cognitive information has been gaining popularity. Researchers use a variety of methods to analyze texts, but Linguistic Inquiry Word Count (LIWC) has proved to be the most common technique. The analysis of linguistic patterns of political discourse shows that in the context of political speech events such as media interviews, politicians make a unique choice of lexical units, which can be interpreted as a manifestation of certain personality traits. However, despite the significance of the results, there are clear limitations to the use of computerized methodologies to make political discourse content-analysis, such as the limited interpretive capacity of software to understand pragmatic and contextual use of lexical units.read more
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References
More filters
Journal ArticleDOI
Supervised Learning for Fake News Detection
Julio Cesar dos Reis,Andre Correia,Fabricio Murai,Adriano Veloso,Fabrício Benevenuto,Erik Cambria +5 more
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Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
TL;DR: JODIE as mentioned in this paper employs two recurrent neural networks to update the embedding of a user and an item at every interaction, which can be used to predict future user-item interactions.
Journal ArticleDOI
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Journal ArticleDOI
Automatic detection of cyberbullying in social media text
Cynthia Van Hee,Gilles Jacobs,Chris Emmery,Bart Desmet,Els Lefever,Ben Verhoeven,Guy De Pauw,Walter Daelemans,Veronique Hoste +8 more
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