APA 6th Edition Jeđud, I. (2018). Interdisciplinary Approach to Emotion Detection from Text. XA Proceedings, 1 (1), 34-72. Retrieved from https://hrcak.srce.hr/200182
MLA 8th Edition Jeđud, Ivica. "Interdisciplinary Approach to Emotion Detection from Text." XA Proceedings, vol. 1, no. 1, 2018, pp. 34-72. https://hrcak.srce.hr/200182. Accessed 25 Feb. 2021.
Chicago 17th Edition Jeđud, Ivica. "Interdisciplinary Approach to Emotion Detection from Text." XA Proceedings 1, no. 1 (2018): 34-72. https://hrcak.srce.hr/200182
Harvard Jeđud, I. (2018). 'Interdisciplinary Approach to Emotion Detection from Text', XA Proceedings, 1(1), pp. 34-72. Available at: https://hrcak.srce.hr/200182 (Accessed 25 February 2021)
Vancouver Jeđud I. Interdisciplinary Approach to Emotion Detection from Text. XA Proceedings [Internet]. 2018 [cited 2021 February 25];1(1):34-72. Available from: https://hrcak.srce.hr/200182
IEEE I. Jeđud, "Interdisciplinary Approach to Emotion Detection from Text", XA Proceedings, vol.1, no. 1, pp. 34-72, 2018. [Online]. Available: https://hrcak.srce.hr/200182. [Accessed: 25 February 2021]
Abstracts Emotions not only influence most aspects of cognition and behavior, but also play a prominent role in interaction and communication between people. With current multidimensional research on emotions being vast and varied, all researchers of emotions, both psychologists and linguists alike, agree that emotions are at the core of understanding ourselves and others. As a primary vehicle of communication and interaction, language is the most convenient medium for approaching research on the topic of emotions. Not only is one of the main functions of language the emotive one, but the interplay of emotions and language occurs at all linguistic levels. Textual data, in particular, can be beneficial to emotion detection due to its syntactic and semantic information containing not only informative content, but emotional states as well. A general overview of the emotion models based on the research in psychology, as well as the major approaches to emotion detection from text found in linguistics, together with usage demonstration of emotion detection linguistic tools, will be given in this paper. Examples of useful applications – from psychologists analyzing session transcripts in search for any subtle emotions, over public opinion mining on social networks to the development of AI technology – will also be provided showing that emotion detection from text has an abundance of practical uses. As the methods for emotion detection from text become more accurate, uses and applications of emotion detection from text will become more numerous and diverse in the future.