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Does the Frequency of Using Emoticons in Computer-Mediated Communication Signal Creativity?

REFERENCES Baas, M., De Dreu, C. K., & Nijstad, B. A. (2008). A meta-analysis of 25 years of mood-creativity research: Hedonic tone, activation, or regulatory focus?. Psychological Bulletin, 134 (6), 779-806. Baron, N. S. (2004). See you online: Gender issues in college student use of instant messaging. Journal of Language and Social Psychology , 23 (4), 397-423. Boldea, O. & Norley, N. (2008). Emoticons and their role in computer-mediated communication. Journal

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The review starts by stating that modern communication technologies do, without doubt, influence several domains of human behaviour, including the domain of language. The reviewed title focus on peculiarities with one of these new ways of communication, namely, communication on two prominent social networks. The review gives an overview over structure in the book, and comments on its scope. The core of the books’ research is located in the second part of the monograph, where changes in ortography and some other features, such as use of emoticons, are observed in excerpts from Facebook and Twitter corpora, counting 18 000 tokens both. The review concludes that typology of ortographical differences, proposed by the book, is accurate and reflects the real situation.

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Emotions Experienced by Learners and their Development through Communication with the Tutor-Counsellor

). Engagement, excitement, anxiety and fear. Learners’ experiences of starting an online course. The American Journal of Distance Education, 16 (4), 205-226. 6. Damasio, A. (1994). Descartes’ error: emotions, reason, and the human brain. New York: Avon Books. 7. Derks, D. (2007). Exploring the Missing Wink: Emoticons in Cyberspace. Dissertation. Open University, The Nederlands. 8. Derks, D., Fischer, A.H., & Bos, A.E.R. (2008). The role of emotion in computer-mediated communication: A review. Computer Human Behavior, 24 (3), 766-785. 9. Dirkx, J

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The Social Presences in Text-Based Collaborations Via Electronic Devices: Measuring the ‘Online-Self’ of the Young Generation in Bangladesh

., & Von Grumbkow, J. (2007). Emoticons and social interaction on the Internet: the importance of social context. Computers in human behavior , 23 (1), 842-849. Donath, J. S. (1995, January). Visual Who: Animating the affinities and activities of an electronic community. In Proceedings of the third ACM international conference on Multimedia (pp. 99-107). ACM. Douglas, K. M., & McGarty, C. (2001). Identifiability and self-presentation: Computer-mediated communication and intergroup interaction. British journal of social psychology, 40 (3), 399

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Irony Comprehension in the Nonnative Language Comes at a Cost

., Wallington, K., & Page, J. (2014). Testing Theories of Irony Processing Using Eye-Tracking and ERPs. Journal of Experimental Psychology: Learning, Memory, and Cognition , 40 (3), 811–828. Filik, R., Ţurcan, A., Thompson, D., Harvey, N., Davies H., & Turner A. (2016). Sarcasm and emoticons: Comprehension and emotional impact. The Quarterly Journal of Experimental Psychology , 69 (11), 2130–2146. Gerrig, R. & Goldvarg, Y. (2000). Additive effects in the perception of sarcasm: Situational disparity and echoic mention. Metaphor and Symbol , 15 (4), 197

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Sentiment Analysis of Japanese Tourism Online Reviews

a great variety of reviews uploaded by Japanese travelers. We conduct sentiment analysis of Japanese reviews on Chinese attractions utilizing statistics- and rule-based methods. Three groups of novel methods combining feature selection functions and the traditional term frequency-inverse document frequency (TF-IDF) method are proposed. Additional sentiment words and emoticons are chosen to complement the original emotional dictionary and an extensive comparative study is performed. Compared with existing studies, the main contributions and the innovation of our

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Evaluating stance-annotated sentences from the Brexit Blog Corpus: A quantitative linguistic analysis

.). Modality in contemporary English (Topics in English Linguistics 44), 191–222. Berlin: DeGruyter. Park, Jaram, Young Min Baek and Meeyoung Cha. 2014. Cross-cultural comparison of nonverbal cues in emoticons on twitter: Evidence from big data analysis. Journal of Communication 64 (2): 333–354. Paterson, Laura L., Laura Coffey-Glover and David Peplow. 2016. Negotiating stance within discourses of class: Reactions to Benefits Street. Discourse & Society 27 (2): 195–214. Peersman, Claudia, Walter Daelemans and Leona Van Vaerenbergh. 2011. Predicting age

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