Article

Automated mark up of affective information in english texts

Authors:

Virginia Francisco,

Pablo GervásAuthors Info & Claims

TSD'06: Proceedings of the 9th international conference on Text, Speech and Dialogue

Pages 375 - 382

https://doi.org/10.1007/11846406_47

Published: 11 September 2006 Publication History

Publisher Site

Abstract

This paper presents an approach to automated marking up of texts with emotional labels The approach considers in parallel two possible representations of emotions: as emotional categories and emotional dimensions For each representation, a corpus of example texts previously annotated by human evaluators is mined for an initial assignment of emotional features to words This results in a List of Emotional Words (LEW) which becomes a useful resource for later automated mark up The proposed algorithm for automated mark up of text mirrors closely the steps taken during feature extraction, employing for the actual assignment of emotional features a combination of the LEW resource, the ANEW word list, and WordNet for knowledge-based expansion of words not occurring in either The algorithm for automated mark up is tested and the results are discussed with respect to three main issues: relative adequacy of each one of the representations used, correctness and coverage of the proposed algorithm, and additional techniques and solutions that may be employed to improve the results.

References

[1]

Cowie, R., Cornelius, R.: Describing the emotional states that are expressed in speech In: Speech Communication Special Issue on Speech and Emotion (2003).

Digital Library

Google Scholar

[2]

Alter, K., Rank, E., Kotz, S., Toepel, U., Besson, M., Schirmer, A., Friederici, A.: Accentuation and emotions--two different systems? In: Proceedings of the ISCA Workshop on Speech and Emotion, Northern Ireland (2000) 138-142.

Google Scholar

[3]

Russell, J.: A circumflex model of affect Journal of Personality and Social Psychology 39 (1980) 1161-1178.

Crossref

Google Scholar

[4]

Liu, H Lieberman, H., Selker, T.: A model of textual affect sensing using real-world knowledge In: Proceedings of IUI, Miami, Florida (2003).

Digital Library

Google Scholar

[5]

Ortony, A., Clore, G., Collins, A.: The cognitive structure of emotions Cambridge University Press, New York (1988).

Google Scholar

[6]

Goertzel, B., Silverman, K., Hartley, C., Bugaj, S., Ross, M.: The baby webmind project In: Proceedings of AISB (2000).

Google Scholar

[7]

Lang, P.: Behavioural treatment and bio-behavioural assessment: Computer applications In Sidowski, J.B., Johnson, J.H., (Eds.), T.A.W., eds.: Technology in mental health care delivery systems, Norwood, NJ, Ablex Publishing (1980) 119-137.

Google Scholar

[8]

Porter, M.: An algorithm for suffix stripping In: Readings in information retrieval, San Francisco, CA, USA, Morgan Kaufmann Publishers Inc A (1997) 313-316.

Digital Library

Google Scholar

[9]

Miller, G.: Wordnet: a lexical database for English Communications of the ACM 38 (1995) 39-41.

Digital Library

Google Scholar

[10]

Bradley, M., Lang, P.: Affective norms for English words (ANEW): Stimuli, instruction manual and affective ratings Technical report c-1 Technical report, The Center for Research in Psychophysiology, University of Florida (1999).

Google Scholar

Cited By

View all

Purpura AMasiero CSilvello GAntonio Susto G(2019)Supervised Lexicon Extraction for Emotion ClassificationCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316700(1071-1078)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3316700
Mohammad SAgirre E(2012)#Emotional tweetsProceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation10.5555/2387636.2387676(246-255)Online publication date: 7-Jun-2012
https://dl.acm.org/doi/10.5555/2387636.2387676
Al-Taani AAbualhaija SRamadan SAbuhaija IJarrah OQuwaider M(2012)Poetry expert systemProceedings of the 3rd International Conference on Information and Communication Systems10.1145/2222444.2222459(1-5)Online publication date: 3-Apr-2012
https://dl.acm.org/doi/10.1145/2222444.2222459

Recommendations

Affective Learning: Empathetic Agents with Emotional Facial and Tone of Voice Expressions

Empathetic behavior has been suggested to be one effective way for Embodied Conversational Agents (ECAs) to provide feedback to learners' emotions. An issue that has been raised is the effective integration of parallel and reactive empathy. The aim of ...
Application of Affective Computing in Sentiment Analysis of English Writing in College Students
IECT '24: Proceedings of the 2024 International Conference on Intelligent Education and Computer Technology

Affective computing plays an important role in teaching and learning. The 47 college students’ writing texts were deeply mined with the help of affective computing technology. The results showed that all the 47 texts had positive emotional tendency, 77.9%...
Affective guidance of intelligent agents: How emotion controls cognition

How do emotions and moods color cognition? In this article, we examine how such reactions influence both judgments and cognitive performance. We argue that many affective influences are due, not to affective reactions themselves, but to the information ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

TSD'06: Proceedings of the 9th international conference on Text, Speech and Dialogue

September 2006

718 pages

ISBN:3540390901

Editors:
Petr Sojka
Faculty of Informatics, Masaryk University, Brno, Czech Republic
,
Ivan Kopeček
Faculty of Informatics, Masaryk University, Botanická 68a, Brno, Czech Republic
,
Karel Pala
Faculty of Informatics, Department of Computer Graphics and Design, Masaryk University, Botanická 68a, Brno, Czech Republic

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 11 September 2006

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Purpura AMasiero CSilvello GAntonio Susto G(2019)Supervised Lexicon Extraction for Emotion ClassificationCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316700(1071-1078)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3316700
Mohammad SAgirre E(2012)#Emotional tweetsProceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation10.5555/2387636.2387676(246-255)Online publication date: 7-Jun-2012
https://dl.acm.org/doi/10.5555/2387636.2387676
Al-Taani AAbualhaija SRamadan SAbuhaija IJarrah OQuwaider M(2012)Poetry expert systemProceedings of the 3rd International Conference on Information and Communication Systems10.1145/2222444.2222459(1-5)Online publication date: 3-Apr-2012
https://dl.acm.org/doi/10.1145/2222444.2222459

Abstract

References

Cited By

Recommendations

Affective Learning: Empathetic Agents with Emotional Facial and Tone of Voice Expressions

Application of Affective Computing in Sentiment Analysis of English Writing in College Students

Affective guidance of intelligent agents: How emotion controls cognition

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Share

Share this Publication link

Share on social media

Affiliations