{"id":"https://openalex.org/W106645950","doi":null,"title":"A Hybrid AI Approach to the Classification of Emotive Text","display_name":"A Hybrid AI Approach to the Classification of Emotive Text","publication_year":2009,"publication_date":"2009-01-01","ids":{"openalex":"https://openalex.org/W106645950","mag":"106645950"},"language":"en","primary_location":{"id":"mag:106645950","doi":null,"is_oa":false,"landing_page_url":"https://dblp.uni-trier.de/db/conf/icai/icai2009.html#Guinn09","pdf_url":null,"source":{"id":"https://openalex.org/S4306419142","display_name":"International Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Artificial Intelligence","raw_type":null},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000292579","display_name":"Curry I. Guinn","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I153901656","display_name":"University of North Carolina Wilmington","ror":"https://ror.org/02t0qr014","country_code":"US","type":"education","lineage":["https://openalex.org/I153901656"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Curry Guinn","raw_affiliation_strings":["University of North Carolina Wilmington, Wilmington, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Carolina Wilmington, Wilmington, United States","institution_ids":["https://openalex.org/I153901656"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5000292579"],"corresponding_institution_ids":["https://openalex.org/I153901656"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"156","last_page":"160"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9643999934196472,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9643999934196472,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.023099999874830246,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12488","display_name":"Mental Health via Writing","score":0.007899999618530273,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/emotion-classification","display_name":"emotion classification","score":0.6840000152587891},{"id":"https://openalex.org/keywords/autobiographical-writing","display_name":"autobiographical writing","score":0.9290000200271606},{"id":"https://openalex.org/keywords/bayesian-classification","display_name":"Bayesian classification","score":0.7089999914169312},{"id":"https://openalex.org/keywords/hybrid-classification-system","display_name":"hybrid classification system","score":0.7070000171661377}],"concepts":[{"id":"https://openalex.org/C2776215170","wikidata":"https://www.wikidata.org/wiki/Q5373820","display_name":"Emotive","level":2,"score":0.93046635389328},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7896543741226196},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7381414771080017},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6695483922958374},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.351912260055542},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:106645950","doi":null,"is_oa":false,"landing_page_url":"https://dblp.uni-trier.de/db/conf/icai/icai2009.html#Guinn09","pdf_url":null,"source":{"id":"https://openalex.org/S4306419142","display_name":"International Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Artificial Intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W29080963","https://openalex.org/W45626652","https://openalex.org/W182831726","https://openalex.org/W229337609","https://openalex.org/W1738371459","https://openalex.org/W2281492572","https://openalex.org/W2339343773","https://openalex.org/W2995034616"],"related_works":["https://openalex.org/W4410494020","https://openalex.org/W4309217541","https://openalex.org/W4310007158","https://openalex.org/W280593287","https://openalex.org/W2020864002","https://openalex.org/W4399463308","https://openalex.org/W7165128813","https://openalex.org/W2794667363","https://openalex.org/W7091492696","https://openalex.org/W2932079797"],"abstract_inverted_index":{"In":[0,23,54],"this":[1],"paper":[2,68],"we":[3,25],"will":[4,26],"discuss":[5],"Natural":[6],"Language":[7],"Processing":[8],"(NLP)":[9],"techniques":[10,31],"used":[11],"to":[12,59,73,78],"classify":[13],"the":[14],"emotional":[15],"content":[16,52],"of":[17,20,49,64,93],"a":[18,60,91],"segment":[19],"autobiographical":[21,96],"text.":[22],"particular":[24],"examine":[27],"1)":[28],"statistical":[29],"training":[30],"which":[32,44,83],"rely":[33,45],"on":[34,46],"Bayesian":[35],"models":[36],"for":[37,90],"classification,":[38],"and":[39,51],"2)":[40],"statisti":[41],"cal":[42],"analyses":[43],"hand-built":[47],"annotations":[48],"emotive":[50],"keywords.":[53],"isolation,":[55],"each":[56],"methodology":[57],"performs":[58],"relatively":[61],"high":[62],"level":[63],"precision":[65],"(85-90%).":[66],"This":[67],"then":[69],"looks":[70],"at":[71],"how":[72],"combine":[74],"these":[75],"two":[76],"approaches":[77,88],"produce":[79],"an":[80,85],"overall":[81],"system":[82],"has":[84],"accuracy":[86],"that":[87],"98%":[89],"corpus":[92],"over":[94],"285":[95],"entries.":[97]},"counts_by_year":[],"updated_date":"2026-09-22T06:50:58.402377","created_date":"2016-06-24T00:00:00"}