Affect analysis in context of characters in narratives
► We perform research in character/person-based textual affect analysis of narratives. ► We propose a method for subject extraction from sentence based on anaphora. ► We compare two methods for affect analysis, first using WordNet, second using ML-Ask. ► The evaluation showed significant improvement...
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| Veröffentlicht in: | Expert systems with applications 2013-01, Vol.40 (1), p.168-176 |
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| container_title | Expert systems with applications |
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| creator | Ptaszynski, Michal Dokoshi, Hiroaki Oyama, Satoshi Rzepka, Rafal Kurihara, Masahito Araki, Kenji Momouchi, Yoshio |
| description | ► We perform research in character/person-based textual affect analysis of narratives. ► We propose a method for subject extraction from sentence based on anaphora. ► We compare two methods for affect analysis, first using WordNet, second using ML-Ask. ► The evaluation showed significant improvement with the use of ML-Ask over WordNet.
This paper presents our research in text-based affect analysis (AA) of narratives. AA represents a task of estimating or recognizing emotions elicited by a certain semiotic modality. In text-based AA the modality in focus is the textual representation of language. In this research we study particularly one type of language realization, namely narratives (e.g., stories, fairy tales, etc.). Affect analysis within the context of narratives is a challenging task because narratives are created of different kinds of sentences (descriptions, dialogs, etc.). Moreover, different characters become subjects of different emotional expressions in different parts of narratives. In this research we address the problem of person/character related affect recognition in narratives. We propose a method for emotion subject extraction from a sentence based on analysis of anaphoric expressions and compare two methods for affect analysis. We evaluate the system and discuss its possible future improvements. |
| doi_str_mv | 10.1016/j.eswa.2012.07.025 |
| format | Article |
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This paper presents our research in text-based affect analysis (AA) of narratives. AA represents a task of estimating or recognizing emotions elicited by a certain semiotic modality. In text-based AA the modality in focus is the textual representation of language. In this research we study particularly one type of language realization, namely narratives (e.g., stories, fairy tales, etc.). Affect analysis within the context of narratives is a challenging task because narratives are created of different kinds of sentences (descriptions, dialogs, etc.). Moreover, different characters become subjects of different emotional expressions in different parts of narratives. In this research we address the problem of person/character related affect recognition in narratives. We propose a method for emotion subject extraction from a sentence based on analysis of anaphoric expressions and compare two methods for affect analysis. We evaluate the system and discuss its possible future improvements.</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2012.07.025</identifier><language>eng</language><publisher>Amsterdam: Elsevier Ltd</publisher><subject>Affect analysis ; Applied sciences ; Artificial intelligence ; Character recognition ; Computer science; control theory; systems ; Descriptions ; Emotions ; Exact sciences and technology ; Expert systems ; Narratives ; Natural Language Processing ; Pattern recognition. Digital image processing. Computational geometry ; Recognition ; Sentences ; Speech and sound recognition and synthesis. Linguistics ; Tasks</subject><ispartof>Expert systems with applications, 2013-01, Vol.40 (1), p.168-176</ispartof><rights>2012 Elsevier Ltd</rights><rights>2014 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c429t-a258d10e26ec71437226354c1a24c2b68c774bb6e401e9334bd1315955c7e3f73</citedby><cites>FETCH-LOGICAL-c429t-a258d10e26ec71437226354c1a24c2b68c774bb6e401e9334bd1315955c7e3f73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.eswa.2012.07.025$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3548,4022,27922,27923,27924,45994</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27095895$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Ptaszynski, Michal</creatorcontrib><creatorcontrib>Dokoshi, Hiroaki</creatorcontrib><creatorcontrib>Oyama, Satoshi</creatorcontrib><creatorcontrib>Rzepka, Rafal</creatorcontrib><creatorcontrib>Kurihara, Masahito</creatorcontrib><creatorcontrib>Araki, Kenji</creatorcontrib><creatorcontrib>Momouchi, Yoshio</creatorcontrib><title>Affect analysis in context of characters in narratives</title><title>Expert systems with applications</title><description>► We perform research in character/person-based textual affect analysis of narratives. ► We propose a method for subject extraction from sentence based on anaphora. ► We compare two methods for affect analysis, first using WordNet, second using ML-Ask. ► The evaluation showed significant improvement with the use of ML-Ask over WordNet.
This paper presents our research in text-based affect analysis (AA) of narratives. AA represents a task of estimating or recognizing emotions elicited by a certain semiotic modality. In text-based AA the modality in focus is the textual representation of language. In this research we study particularly one type of language realization, namely narratives (e.g., stories, fairy tales, etc.). Affect analysis within the context of narratives is a challenging task because narratives are created of different kinds of sentences (descriptions, dialogs, etc.). Moreover, different characters become subjects of different emotional expressions in different parts of narratives. In this research we address the problem of person/character related affect recognition in narratives. We propose a method for emotion subject extraction from a sentence based on analysis of anaphoric expressions and compare two methods for affect analysis. We evaluate the system and discuss its possible future improvements.</description><subject>Affect analysis</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Character recognition</subject><subject>Computer science; control theory; systems</subject><subject>Descriptions</subject><subject>Emotions</subject><subject>Exact sciences and technology</subject><subject>Expert systems</subject><subject>Narratives</subject><subject>Natural Language Processing</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Recognition</subject><subject>Sentences</subject><subject>Speech and sound recognition and synthesis. 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Digital image processing. Computational geometry</topic><topic>Recognition</topic><topic>Sentences</topic><topic>Speech and sound recognition and synthesis. Linguistics</topic><topic>Tasks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ptaszynski, Michal</creatorcontrib><creatorcontrib>Dokoshi, Hiroaki</creatorcontrib><creatorcontrib>Oyama, Satoshi</creatorcontrib><creatorcontrib>Rzepka, Rafal</creatorcontrib><creatorcontrib>Kurihara, Masahito</creatorcontrib><creatorcontrib>Araki, Kenji</creatorcontrib><creatorcontrib>Momouchi, Yoshio</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Expert systems with applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ptaszynski, Michal</au><au>Dokoshi, Hiroaki</au><au>Oyama, Satoshi</au><au>Rzepka, Rafal</au><au>Kurihara, Masahito</au><au>Araki, Kenji</au><au>Momouchi, Yoshio</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Affect analysis in context of characters in narratives</atitle><jtitle>Expert systems with applications</jtitle><date>2013-01</date><risdate>2013</risdate><volume>40</volume><issue>1</issue><spage>168</spage><epage>176</epage><pages>168-176</pages><issn>0957-4174</issn><eissn>1873-6793</eissn><abstract>► We perform research in character/person-based textual affect analysis of narratives. ► We propose a method for subject extraction from sentence based on anaphora. ► We compare two methods for affect analysis, first using WordNet, second using ML-Ask. ► The evaluation showed significant improvement with the use of ML-Ask over WordNet.
This paper presents our research in text-based affect analysis (AA) of narratives. AA represents a task of estimating or recognizing emotions elicited by a certain semiotic modality. In text-based AA the modality in focus is the textual representation of language. In this research we study particularly one type of language realization, namely narratives (e.g., stories, fairy tales, etc.). Affect analysis within the context of narratives is a challenging task because narratives are created of different kinds of sentences (descriptions, dialogs, etc.). Moreover, different characters become subjects of different emotional expressions in different parts of narratives. In this research we address the problem of person/character related affect recognition in narratives. We propose a method for emotion subject extraction from a sentence based on analysis of anaphoric expressions and compare two methods for affect analysis. We evaluate the system and discuss its possible future improvements.</abstract><cop>Amsterdam</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2012.07.025</doi><tpages>9</tpages></addata></record> |
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| source | Elsevier ScienceDirect Journals Complete |
| subjects | Affect analysis Applied sciences Artificial intelligence Character recognition Computer science control theory systems Descriptions Emotions Exact sciences and technology Expert systems Narratives Natural Language Processing Pattern recognition. Digital image processing. Computational geometry Recognition Sentences Speech and sound recognition and synthesis. Linguistics Tasks |
| title | Affect analysis in context of characters in narratives |
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