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

research-article

Intelligent Interface for Textual Attitude Analysis

Authors:

Alena Neviarouskaya,

Helmut Prendinger,

Mitsuru IshizukaAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 5, Issue 3

Article No.: 48, Pages 1 - 20

https://doi.org/10.1145/2535912

Published: 18 September 2014 Publication History

Abstract

This article describes a novel intelligent interface for attitude sensing in text driven by a robust computational tool for the analysis of fine-grained attitudes (emotions, judgments, and appreciations) expressed in text. The module responsible for textual attitude analysis was developed using a compositional linguistic approach based on the attitude-conveying lexicon, the analysis of syntactic and dependency relations between words in a sentence, the compositionality principle applied at various grammatical levels, the rules elaborated for semantically distinct verb classes, and a method considering the hierarchy of concepts. The performance of this module was evaluated on sentences from personal stories about life experiences. The developed web-based interface supports recognition of nine emotions, positive and negative judgments, and positive and negative appreciations conveyed in text. It allows users to adjust parameters, to enable or disable various functionality components of the algorithm, and to select the format of text annotation and attitude statistics visualization.

References

[1]

Cecilia O. Alm. 2008. Affect in Text and Speech. Ph.D. dissertation. University of Illinois at Urbana, Champaign.

[2]

Saima Aman and Stan Szpakowicz. 2008. Using Roget's Thesaurus for fine-grained emotion recognition. In Proceedings of the IJCNLP 2008, 296--302.

[3]

Anthony C. Boucouvalas. 2003. Real time text-to-emotion engine for expressive internet communications. In Being There: Concepts, Effects and Measurement of User Presence in Synthetic Environments. Ios Press, 306--318.

[4]

Michael Brooks, Katie Kuksenok, Megan K. Torkildson, Daniel Perry, John J. Robinson, Taylor J. Scott, Ona Anicello, Ariana Zukowski, Paul Harris, and Cecilia R. Aragon. 2013. Statistical affect detection in collaborative chat. In Proceedings of the 2013 Conference on Computer Supported Cooperative Work, 317--328.

Digital Library

[5]

Carmine Cesarano, Bonnie Dorr, Antonio Picariello, Diego Reforgiato, Amelia Sagoff, and V. S. Subrahmanian. 2006. OASYS: An opinion analysis system. In Proceedings of the AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs, 21--26.

[6]

François-Régis Chaumartin. 2007. UPAR7: A knowledge-based system for headline sentiment tagging. In Proceedings of the SemEval-2007, 422--425.

Digital Library

[7]

Munmun De Choudhury, Scott Counts, and Michael Gamon. 2012. Not all moods are created equal&excl; Exploring human emotional states in social media. In Proceedings of the International AAAI Conference on Weblogs and Social Media 2012.

[8]

Ze-Jing Chuang and Chung-Hsien Wu. 2004. Multi-modal emotion recognition from speech and text. Computational Linguistics and Chinese Language Processing 9, 2 (2004), 45--62.

[9]

Yejin Choi and Claire Cardie. 2008. Learning with compositional semantics as structural inference for subsentential sentiment analysis. In Proceedings of the EMNLP 2008, 793--801.

Digital Library

[10]

Jenny R. Finkel, Trond Grenager, and Christopher Manning. 2005. Incorporating non-local information into information extraction systems by Gibbs sampling. In Proceedings of the 43rd Annual Meeting of the ACL, ACL (2005), 363--370.

Digital Library

[11]

Joseph L. Fleiss. 1971. Measuring nominal scale agreement among many raters. Psychological Bulletin 76, 5 (1971), 378--382.

[12]

Erik Frøkjær, Morten Hertzum, and Kasper Hornbæk. 2000. Measuring usability: Are effectiveness, efficiency and satisfaction really correlated&quest; In Proceedings of the ACM CHI Conference on Human Factors in Computing Systems 2000, ACM Press (2000), 345--352.

Digital Library

[13]

Diman Ghazi, Diana Inkpen, and Stan Szpakowicz. 2010. Hierarchical versus flat classification of emotions in text. In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, 140--146.

Digital Library

[14]

Carroll E. Izard. 1971. The Face of Emotion. New York: Appleton-Century-Crofts.

[15]

Wei Jin, Hung H. Ho, and Rohini K. Srihari. 2009. OpinionMiner: a novel machine learning system for web opinion mining and extraction. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press (2009), 1195--1204.

Digital Library

[16]

Phil Katz, Matt Singleton, and Richard Wicentowski. 2007. SWAT-MP: The SemEval-2007 systems for task 5 and task 14. In Proceedings of the SemEval-2007, 308--313.

Digital Library

[17]

Soo-Min Kim and Eduard Hovy. 2005. Automatic detection of opinion bearing words and sentences. In Proceedings of the IJCNLP 2005, LNCS 3651, Springer, Berlin, 61--66.

[18]

Karin Kipper, Anna Korhonen, Neville Ryant, and Martha Palmer. 2007. A large-scale classification of English verbs. Language Resources and Evaluation 42, 1 (2007), 21--40.

[19]

Bing Liu, Minqing Hu, and Junsheng Cheng. 2005. Opinion Observer: Analyzing and comparing opinions on the web. In Proceedings of the 14th International World Wide Web Conference (WWW-2005), 342--351.

Digital Library

[20]

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

Digital Library

[21]

James R. Martin, and Peter R. R. White. 2005. The Language of Evaluation: Appraisal in English, London, UK: Palgrave.

[22]

I-Heng Mei, Hongcheng Mi, and Julius Quiaot. 2007. Sentiment mining and indexing in Opinmind. In Proceedings of the ICWSM 2007.

[23]

George A. Miller. Nouns in WordNet. 1999. In C. Fellbaum (ed.), WordNet: An Electronic Lexical Database, Cambridge: MIT Press (1999), 23--46.

[24]

Karo Moilanen and Stephen Pulman. 2007. Sentiment composition. In Proceedings of the RANLP 2007, 378--382.

[25]

Matthijs Mulder, Anton Nijholt, Marten den Uyl, and Peter Terpstra. 2004. A lexical grammatical implementation of affect. In Proceedings of the TSD 2004, Springer, Berlin, 171--178.

[26]

Tetsuji Nakagawa, Kentaro Inui, and Sadao Kurohashi. 2010. Dependency tree-based sentiment classification using CRFs with hidden variables. In Proceedings of the NAACL-HLT 2010, ACL (2010), 786--794.

Digital Library

[27]

Tetsuya Nasukawa and Jeonghee Yi. 2003. Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the K-CAP 2003, 70--77.

Digital Library

[28]

Alena Neviarouskaya, Helmut Prendinger, and Mitsuru Ishizuka. 2010. Recognition of affect, judgment, and appreciation in text. In Proceedings of the International Conference on Computational Linguistics (COLING 2010), 806--814.

Digital Library

[29]

Jimena Olveres, Mark Billinghurst, Jesus Savage, and Alistair Holden. 1998. Intelligent, expressive avatars. In Proceedings of the 1st Workshop on Embodied Conversational Characters, 47--55.

[30]

Siddarth Shankar, Ramaswamy. 2011. Visualization of the Sentiment of the Tweets. Master thesis. North Carolina State University.

[31]

Jonathon Read. 2005. Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In Proceedings of the ACL Student Research Workshop at ACL 2005, 43--48.

Digital Library

[32]

Mostafa Al Masum Shaikh, Helmut Prendinger, and Mitsuru Ishizuka. 2009. A linguistic interpretation of the OCC emotion model for affect sensing from text. In J. Tao and T. Tan (eds.), Affective Information Processing, Springer-Verlag London, 45--73.

[33]

Carlo Strapparava and Rada Mihalcea. 2008. Learning to identify emotions in text. In Proceedings of the ACM Symposium on Applied Computing, 1556--1560.

Digital Library

[34]

Carlo Strapparava, Alessandro Valitutti, and Oliviero Stock. 2007. Dances with words. In Proceedings of the IJCAI 2007, 1719--1724.

Digital Library

[35]

V. S. Subrahmanian and Diego Reforgiato. 2008. AVA: Adjective-verb-adverb combinations for sentiment analysis. IEEE Intelligent Systems 23, 4 (2008), 43--50.

Digital Library

[36]

Dzmitry Tsetserukou and Alena Neviarouskaya. 2010. iFeel_IM&excl;: Augmenting emotions during online communication. IEEE Computer Graphics and Applications 30, 5 (2010), 72--80.

Digital Library

[37]

Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the HLT/EMNLP 2005, ACL (2005), 347--354.

Digital Library

[38]

Hong Yu and Vasileios Hatzivassiloglou. 2003. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In Proceedings of the EMNLP 2003, ACL (2003), 129--136.

Digital Library

Cited By

Mahafdah RDardouri SBouallegue R(2023)A Systematic Review on the Applications of IoT and Artificial Intelligence in learning2023 IEEE International Workshop on Mechatronic Systems Supervision (IW_MSS)10.1109/IW_MSS59200.2023.10369284(1-7)Online publication date: 2-Nov-2023
https://doi.org/10.1109/IW_MSS59200.2023.10369284
Mridha MRahman MOhi A(2020)Human Abnormality Detection Based on Bengali Text2020 IEEE Region 10 Symposium (TENSYMP)10.1109/TENSYMP50017.2020.9230629(1102-1105)Online publication date: 5-Jun-2020
https://doi.org/10.1109/TENSYMP50017.2020.9230629
Rodrigues RCamilo-Junior CRosa T(2018)A Taxonomy for Sentiment Analysis FieldInternational Journal of Web Information Systems10.1108/IJWIS-07-2017-0048(00-00)Online publication date: 2-May-2018
https://doi.org/10.1108/IJWIS-07-2017-0048
Show More Cited By

Index Terms

Intelligent Interface for Textual Attitude Analysis
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

Attitude Sensing in Text Based on A Compositional Linguistic Approach

In this article, we introduce a compositional linguistic approach for attitude recognition in text. There are several aspects that distinguish our attitude analysis model @AM from other systems. First, our method classifies sentences using fine-grained ...
Analysis of affect expressed through the evolving language of online communication
IUI '07: Proceedings of the 12th international conference on Intelligent user interfaces

In this paper, we focus on affect recognition from text in order to facilitate sensitive and expressive communication in computer-mediated environments. Our model for analyzing affect conveyed by text is tailored to handle the style and specifics of ...
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

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 5, Issue 3

Special Section on Urban Computing

September 2014

361 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2648782

Editor:
Qiang Yang
Hong Kong University of Science and Technology, Hong Kong

Issue’s Table of Contents

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 September 2014

Accepted: 01 September 2013

Revised: 01 June 2013

Received: 01 February 2013

Published in TIST Volume 5, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Japan Society for the Promotion of Science

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
396
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)2

Reflects downloads up to 26 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Mahafdah RDardouri SBouallegue R(2023)A Systematic Review on the Applications of IoT and Artificial Intelligence in learning2023 IEEE International Workshop on Mechatronic Systems Supervision (IW_MSS)10.1109/IW_MSS59200.2023.10369284(1-7)Online publication date: 2-Nov-2023
https://doi.org/10.1109/IW_MSS59200.2023.10369284
Mridha MRahman MOhi A(2020)Human Abnormality Detection Based on Bengali Text2020 IEEE Region 10 Symposium (TENSYMP)10.1109/TENSYMP50017.2020.9230629(1102-1105)Online publication date: 5-Jun-2020
https://doi.org/10.1109/TENSYMP50017.2020.9230629
Rodrigues RCamilo-Junior CRosa T(2018)A Taxonomy for Sentiment Analysis FieldInternational Journal of Web Information Systems10.1108/IJWIS-07-2017-0048(00-00)Online publication date: 2-May-2018
https://doi.org/10.1108/IJWIS-07-2017-0048
Ortega-Bueno RMedina-Pagola JMuñiz-Cuza CRosso P(2018)Improving Attitude Words Classification for Opinion Mining Using Word EmbeddingProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-030-13469-3_112(971-982)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1007/978-3-030-13469-3_112
Kucher KParadis CKerren A(2017)The State of the Art in Sentiment VisualizationComputer Graphics Forum10.1111/cgf.1321737:1(71-96)Online publication date: 12-Jun-2017
https://doi.org/10.1111/cgf.13217
Teodorescu HSaharia N(2016)A semantic analyzer for detecting attitudes on SNs2016 International Conference on Communications (COMM)10.1109/ICComm.2016.7528201(47-50)Online publication date: Jun-2016
https://doi.org/10.1109/ICComm.2016.7528201

View Options

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 Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents