publication . Article . Software Paper . 2017

ML-Ask: Open Source Affect Analysis Software for Textual Input in Japanese

Rafal Rzepka; Kenji Araki; Pawel Dybala; Michal Ptaszynski; Fumito Masui;
Open Access
  • Published: 07 Jun 2017 Journal: Journal of Open Research Software, volume 5 (eissn: 2049-9647, Copyright policy)
  • Publisher: Ubiquity Press, Ltd.
  • Country: Poland
Abstract
We present ML-Ask – the first Open Source Affect Analysis system for textual input in Japanese. ML-Ask analyses the contents of an input (e.g., a sentence) and annotates it with information regarding the contained general emotive expressions, specific emotional words, valence-activation dimensions of overall expressed affect, and particular emotion types expressed with their respective expressions. ML-Ask also incorporates the Contextual Valence Shifters model for handling negation in sentences to deal with grammatically expressible shifts in the conveyed valence. The system, designed to work mainly under Linux and MacOS, can be used for research on, or applying...
Subjects
free text keywords: affect analysis, Open Source, Japanese, Computer software, QA76.75-76.765, Affect Analysis, Sentiment Analysis, Natural Language Processing, Computational Linguistics, Affect Analysis; Sentiment Analysis; Open Source; Perl; Japanese, Computer science, World Wide Web, Negation, Sentence, Expression (mathematics), Linguistics, Scientific method, Perl, computer.programming_language, computer, Annotation, Emotive, Sentiment analysis
50 references, page 1 of 4

1. Dybala, P, Ptaszynski, M, Rzepka, R and Araki, K 2009 “Activating Humans with Humor? A Dialogue System that Users Want to Interact With”, IEICE Transactions on Information and Systems, E92-D(12): 2394-2401, (December). DOI: https://doi. org/10.1587/transinf.E92.D.2394 [OpenAIRE]

2. Schrempf, O C and Hanebeck, U D 2005 “A generic model for estimating user-intentions in human-robot cooperation.” In: Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics, ICINCO, Vol. 5. 2005. [OpenAIRE]

3. Ptaszynski, M, Dybala, P, Rzepka, R and Araki, K 2010 “An Automatic Evaluation Method for Conversational Agents Based on Affect-as-Information Theory”. Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 22(1): 73-89, (February). DOI: https://doi. org/10.3156/jsoft.22.73

4. Dybala, P, Ptaszynski, M, Higuchi, S, Rzepka, R and Araki, K 2008 Humor Prevails! - Implementing a Joke Generator into a Conversational System. LNAI, 5360: 214-225. DOI: https://doi.org/10.1007/978-3-540- 89378-3_21

5. Grefenstette, G, Qu, Y, Shanahan, J G and Evans, D A 2004 “Coupling Niche Browsers and Affect Analysis for an Opinion Mining”, In: Proceedings of RIAO-04, pp. 186-194.

6. Elliott, C 1992 “The Affective Reasoner - A Process Model of Emotions in a Multi-agent System”, PhD thesis, Northwestern University, May 1992. The Institute for the Learning Sciences, Technical Report No. 32.

7. Liu, H, Lieberman, H and Selker, T 2003 “A Model of Textual Affect Sensing using Real-World Knowledge”, In: Proceedings of IUI 2003, pp. 125-132. DOI: https:// doi.org/10.1145/604045.604067

8. Alm, C O, Roth, D and Sproat, R 2005 “Emotions from text: machine learning for text based emotion prediction”, In: Proc. of HLT/EMNLP, pp. 579-586. DOI: https://doi.org/10.3115/1220575.1220648 [OpenAIRE]

9. Tsuchiya, S, Yoshimura, E, Watabe, H and Kawaoka, T 2007 “The Method of the Emotion Judgement Based on an Association Mechanism”, Journal of Natural Language Processing, 14(3): 219-238. DOI: https://doi.org/10.5715/jnlp.14.3_219

10. Tokuhisa, R, Inui, K and Matsumoto, Y 2008 “Emotion Classification Using Massive Examples Extracted from the Web”, In: Proc. of Coling 2008, pp. 881-888, 2008. DOI: https://doi.org/10.3115/1599081.1599192

11. Shi, W, Rzepka, R and Araki, K 2008 “Emotive Information Discovery from User Textual Input Using Causal Associations from the Internet” [In Japanese], FIT2008, pp. 267-268.

12. Ptaszynski, M, Dybala, P, Shi, W, Rzepka, R and Araki, K 2009 “A System for Affect Analysis of Utterances in Japanese Supported with Web Mining”, Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, Special Issue on Kansei Retrieval, 21(2): 30-49 (194-213), (April).

13. Nakamura, A 1993 “Kanjo hyogen jiten” [Dictionary of Emotive Expressions] (in Japanese), Tokyodo Publishing, Tokyo.

14. Wilson, T and Wiebe, J 2005 Annotating Attributions and Private States. Proceedings of the ACL Workshop on Frontiers in Corpus Annotation II. pp. 53-60. DOI: https://doi.org/10.3115/1608829.1608837

15. Dybala, P, Ptaszynski, M, Rzepka, R and Araki, K Extracting Dajare Candidates from the Web - Japanese Puns Generating System as a Part of Humor Processing Research, In: The Proceedings of the First International Workshop on Laughter in Interaction and Body Movement (LIBM'08), pp. 46-51, Asahikawa, Japan, June 2008.

50 references, page 1 of 4
Abstract
We present ML-Ask – the first Open Source Affect Analysis system for textual input in Japanese. ML-Ask analyses the contents of an input (e.g., a sentence) and annotates it with information regarding the contained general emotive expressions, specific emotional words, valence-activation dimensions of overall expressed affect, and particular emotion types expressed with their respective expressions. ML-Ask also incorporates the Contextual Valence Shifters model for handling negation in sentences to deal with grammatically expressible shifts in the conveyed valence. The system, designed to work mainly under Linux and MacOS, can be used for research on, or applying...
Subjects
free text keywords: affect analysis, Open Source, Japanese, Computer software, QA76.75-76.765, Affect Analysis, Sentiment Analysis, Natural Language Processing, Computational Linguistics, Affect Analysis; Sentiment Analysis; Open Source; Perl; Japanese, Computer science, World Wide Web, Negation, Sentence, Expression (mathematics), Linguistics, Scientific method, Perl, computer.programming_language, computer, Annotation, Emotive, Sentiment analysis
50 references, page 1 of 4

1. Dybala, P, Ptaszynski, M, Rzepka, R and Araki, K 2009 “Activating Humans with Humor? A Dialogue System that Users Want to Interact With”, IEICE Transactions on Information and Systems, E92-D(12): 2394-2401, (December). DOI: https://doi. org/10.1587/transinf.E92.D.2394 [OpenAIRE]

2. Schrempf, O C and Hanebeck, U D 2005 “A generic model for estimating user-intentions in human-robot cooperation.” In: Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics, ICINCO, Vol. 5. 2005. [OpenAIRE]

3. Ptaszynski, M, Dybala, P, Rzepka, R and Araki, K 2010 “An Automatic Evaluation Method for Conversational Agents Based on Affect-as-Information Theory”. Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 22(1): 73-89, (February). DOI: https://doi. org/10.3156/jsoft.22.73

4. Dybala, P, Ptaszynski, M, Higuchi, S, Rzepka, R and Araki, K 2008 Humor Prevails! - Implementing a Joke Generator into a Conversational System. LNAI, 5360: 214-225. DOI: https://doi.org/10.1007/978-3-540- 89378-3_21

5. Grefenstette, G, Qu, Y, Shanahan, J G and Evans, D A 2004 “Coupling Niche Browsers and Affect Analysis for an Opinion Mining”, In: Proceedings of RIAO-04, pp. 186-194.

6. Elliott, C 1992 “The Affective Reasoner - A Process Model of Emotions in a Multi-agent System”, PhD thesis, Northwestern University, May 1992. The Institute for the Learning Sciences, Technical Report No. 32.

7. Liu, H, Lieberman, H and Selker, T 2003 “A Model of Textual Affect Sensing using Real-World Knowledge”, In: Proceedings of IUI 2003, pp. 125-132. DOI: https:// doi.org/10.1145/604045.604067

8. Alm, C O, Roth, D and Sproat, R 2005 “Emotions from text: machine learning for text based emotion prediction”, In: Proc. of HLT/EMNLP, pp. 579-586. DOI: https://doi.org/10.3115/1220575.1220648 [OpenAIRE]

9. Tsuchiya, S, Yoshimura, E, Watabe, H and Kawaoka, T 2007 “The Method of the Emotion Judgement Based on an Association Mechanism”, Journal of Natural Language Processing, 14(3): 219-238. DOI: https://doi.org/10.5715/jnlp.14.3_219

10. Tokuhisa, R, Inui, K and Matsumoto, Y 2008 “Emotion Classification Using Massive Examples Extracted from the Web”, In: Proc. of Coling 2008, pp. 881-888, 2008. DOI: https://doi.org/10.3115/1599081.1599192

11. Shi, W, Rzepka, R and Araki, K 2008 “Emotive Information Discovery from User Textual Input Using Causal Associations from the Internet” [In Japanese], FIT2008, pp. 267-268.

12. Ptaszynski, M, Dybala, P, Shi, W, Rzepka, R and Araki, K 2009 “A System for Affect Analysis of Utterances in Japanese Supported with Web Mining”, Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, Special Issue on Kansei Retrieval, 21(2): 30-49 (194-213), (April).

13. Nakamura, A 1993 “Kanjo hyogen jiten” [Dictionary of Emotive Expressions] (in Japanese), Tokyodo Publishing, Tokyo.

14. Wilson, T and Wiebe, J 2005 Annotating Attributions and Private States. Proceedings of the ACL Workshop on Frontiers in Corpus Annotation II. pp. 53-60. DOI: https://doi.org/10.3115/1608829.1608837

15. Dybala, P, Ptaszynski, M, Rzepka, R and Araki, K Extracting Dajare Candidates from the Web - Japanese Puns Generating System as a Part of Humor Processing Research, In: The Proceedings of the First International Workshop on Laughter in Interaction and Body Movement (LIBM'08), pp. 46-51, Asahikawa, Japan, June 2008.

50 references, page 1 of 4
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