More Web Proxy on the site http://driver.im/

Article

Free access

Emotions from text: machine learning for text-based emotion prediction

Authors:

Cecilia Ovesdotter Alm,

Richard SproatAuthors Info & Claims

HLT '05: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing

Pages 579 - 586

https://doi.org/10.3115/1220575.1220648

Published: 06 October 2005 Publication History

Abstract

In addition to information, text contains attitudinal, and more specifically, emotional content. This paper explores the text-based emotion prediction problem empirically, using supervised machine learning with the SNoW learning architecture. The goal is to classify the emotional affinity of sentences in the narrative domain of children's fairy tales, for subsequent usage in appropriate expressive rendering of text-to-speech synthesis. Initial experiments on a preliminary data set of 22 fairy tales show encouraging results over a naïve baseline and BOW approach for classification of emotional versus non-emotional contents, with some dependency on parameter tuning. We also discuss results for a tripartite model which covers emotional valence, as well as feature set alternations. In addition, we present plans for a more cognitively sound sequential model, taking into consideration a larger set of basic emotions.

References

[1]

Antti Aarne. 1964. The Types of the Folk-Tale: a Classification and Bibliography. Helsinki: Suomalainen Tiedeakatemia.

[2]

Cecilia O. Alm, and Richard Sproat. 2005. Perceptions of emotions in expressive storytelling. INTERSPEECH 2005.

[3]

Xue Bai, Rema Padman, and Edoardo Airoldi. 2004. Sentiment extraction from unstructured text using tabu search-enhanced Markov blankets. In MSW2004, Seattle.

[4]

Philip Beineke, Trevor Hastie, and Shivakumar Vaithyanathan. 2004. The sentimental factor: improving review classification via human-provided information. In Proceedings of ACL, 263--270.

Digital Library

[5]

Janet Cahn. 1990. The generation of affect in synthesized Speech. Journal of the American Voice I/O Society, 8:1--19.

[6]

Andrew Carlson, Chad Cumby, Nicholas Rizzolo, Jeff Rosen, and Dan Roth. 1999. The SNoW Learning Architecture. Technical Report UIUCDCS-R-99-2101, UIUC Comp. Sci.

[7]

Stacey Di Cicco et al. General Inquirer Pos./Neg. lists http://www.webuse.umd.edu:9090/

[8]

Paul Ekman. 1993. Facial expression and emotion. American Psychologist, 48(4), 384--392.

[9]

Christiane Fellbaum, Ed. 1998. WordNet: An Electronic Lexical Database. MIT Press, Cambridge, Mass.

[10]

Vasileios Hatzivassiloglou, and Kathleen McKeown. 1997. Predicting the semantic orientation of adjectives. In Proceedings of ACL, 174--181.

Digital Library

[11]

Philip Johnson-Laird, and Keith Oatley. 1989. The language of emotions: an analysis of a semantic field. Cognition and Emotion, 3:81--123.

[12]

Peter Koomen, Vasin Punyakanok, Dan Roth, and Wen-tau Yih. 2005. Generalized inference with multiple semantic role labeling systems. In Proceedings of the Annual Conference on Computational Language Learning (CoNLL), 181--184.

Digital Library

[13]

Diane Litman, and Kate Forbes-Riley. 2004. Predicting student emotions in computer-human tutoring dialogues. In Proceedings of ACL, 351--358.

Digital Library

[14]

Xin Li, and Dan Roth. 2002. Learning question classifiers: the role of semantic information. In Proc. International Conference on Computational Linguistics (COLING), 556--562.

Digital Library

[15]

Hugo Liu, Henry Lieberman, and Ted Selker. 2003. A model of textual affect sensing using real-world knowledge. In ACM Conference on Intelligent User Interfaces, 125--132.

Digital Library

[16]

Tony Mullen, and Nigel Collier. 2004. Sentiment analysis using support vector machines with diverse information sources. In Proceedings of EMNLP, 412--418.

[17]

Bo Pang, and Lillian Lee. 2004. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of ACL, 271--278.

Digital Library

[18]

Rosalind Picard. 1997. Affective computing. MIT Press, Cambridge, Mass.

Digital Library

[19]

Dan Roth. 1998. Learning to resolve natural language ambiguities: a unified approach. In AAAI, 806--813.

Digital Library

[20]

Klaus Scherer. 2003. Vocal communication of emotion: a review of research paradigms. Speech Commununication, 40(1-2):227--256.

Digital Library

[21]

Greg Siegle. The Balanced Affective Word List http://www.sci.sdsu.edu/CAL/wordlist/words.prn

[22]

Oliver Steele et al. Py-WordNet http://osteele.com/projects/pywordnet/

[23]

Futoshi Sugimoto et al. 2004. A method to classify emotional expressions of text and synthesize speech. In IEEE, 611--614.

[24]

Peter Turney. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of ACL, 417--424.

Digital Library

[25]

Jan van Santen et al. 2003. Applications of computer generated expressive speech for communication disorders. In EUROSPEECH 2003, 1657--1660.

[26]

Janyce Wiebe et al. 2004. Learning subjective language. Journal of Computational Linguistics, 30(3):277--308.

Digital Library

[27]

Theresa Wilson, Janyce Wiebe, and Rebecca Hwa. 2004. Just how mad are you? Finding strong and weak opinion clauses. In Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI), 761--769.

Digital Library

Cited By

Bareiß PKlinger RBarnes JChua TNgo CKumar RLauw HKa-Wei Lee R(2024)English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language PromptsCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651902(1318-1326)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651902
Babanejad NDavoudi HAgrawal AAn APapagelis M(2024)The Role of Preprocessing for Word Representation Learning in Affective TasksIEEE Transactions on Affective Computing10.1109/TAFFC.2023.327011515:1(254-272)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TAFFC.2023.3270115
Su XHuang ZZhao YChen YDou YPan H(2023)Recent Trends in Deep Learning Based Textual Emotion Cause ExtractionIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2023.325416631(2765-2786)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TASLP.2023.3254166
Show More Cited By

Emotions from text: machine learning for text-based emotion prediction
1. Computing methodologies
  1. Artificial intelligence
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

Recommendations

Learning to identify emotions in text
SAC '08: Proceedings of the 2008 ACM symposium on Applied computing

This paper describes experiments concerned with the automatic analysis of emotions in text. We describe the construction of a large data set annotated for six basic emotions: ANGER, DISGUST, FEAR, JOY, SADNESS and SURPRISE, and we propose and evaluate ...
Emotions in Motion: The Combined Effect of Positive and Negative Emotions on Personalised E-Commerce

This research examines the combined effect of positive and negative emotions on online shopping behaviour when customers use personalised services. Data from 421 customers, experiences with personalised online shopping, were used to empirically validate ...
Emotional design and positive emotions in multimedia learning

The present eyetracking study examined the influence of emotions on learning with multimedia. Based on a 2 ý2 experimental design, participants received experimentally induced emotions (positive vs. neutral) and then learned with a multimedia ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image DL Hosted proceedings

HLT '05: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing

October 2005

1054 pages

Conference Chair:
Raymond J. Mooney
The University of Texas at Austin

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 06 October 2005

Qualifiers

Article

Acceptance Rates

HLT '05 Paper Acceptance Rate 127 of 402 submissions, 32%;

Overall Acceptance Rate 240 of 768 submissions, 31%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

92
Total Citations
View Citations
7,375
Total Downloads

Downloads (Last 12 months)169
Downloads (Last 6 weeks)9

Reflects downloads up to 19 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Bareiß PKlinger RBarnes JChua TNgo CKumar RLauw HKa-Wei Lee R(2024)English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language PromptsCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651902(1318-1326)Online publication date: 13-May-2024
Babanejad NDavoudi HAgrawal AAn APapagelis M(2024)The Role of Preprocessing for Word Representation Learning in Affective TasksIEEE Transactions on Affective Computing10.1109/TAFFC.2023.327011515:1(254-272)Online publication date: 1-Jan-2024
Su XHuang ZZhao YChen YDou YPan H(2023)Recent Trends in Deep Learning Based Textual Emotion Cause ExtractionIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2023.325416631(2765-2786)Online publication date: 1-Jan-2023
Tian LOviatt SMuszynski MChamberlain BHealey JSano A(2022)Applied Affective ComputingundefinedOnline publication date: 25-Jan-2022
Ma JHan BLeite F(2021)An automated framework for generating synthetic point clouds from as-built BIM with semantic annotation for scan-to-BIMProceedings of the Winter Simulation Conference10.5555/3522802.3522990(1-10)Online publication date: 13-Dec-2021
Gupta MChandra S(2021)Speech Emotion Recognition Using MFCC and Wide Residual NetworkProceedings of the 2021 Thirteenth International Conference on Contemporary Computing10.1145/3474124.3474171(320-327)Online publication date: 5-Aug-2021
Liu LChen HSun Y(2021)A Multi-Classification Sentiment Analysis Model of Chinese Short Text Based on Gated Linear Units and Attention MechanismACM Transactions on Asian and Low-Resource Language Information Processing10.1145/346442520:6(1-13)Online publication date: 20-Sep-2021
Jan RMohd MHakak N(2020)Enhanced Bootstrapping Algorithm for Automatic Annotation of TweetsInternational Journal of Cognitive Informatics and Natural Intelligence10.4018/IJCINI.202004010314:2(35-60)Online publication date: 1-Apr-2020
Wisniewski PBadillo-Urquiola KAshtorab ZVitak J(2020)Happiness and FearACM Transactions on Social Computing10.1145/34148253:4(1-25)Online publication date: 16-Oct-2020
Maniktala MMiller CMargolese-Malin AJhala AMartens C(2020)M.I.N.U.E.T.: Procedural Musical Accompaniment for Textual NarrativesProceedings of the 15th International Conference on the Foundations of Digital Games10.1145/3402942.3409602(1-7)Online publication date: 15-Sep-2020
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents