{"id":"https://openalex.org/W2250849282","doi":"https://doi.org/10.3115/v1/w14-6809","title":"Segment-based Fine-grained Emotion Detection for Chinese Text","display_name":"Segment-based Fine-grained Emotion Detection for Chinese Text","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2250849282","doi":"https://doi.org/10.3115/v1/w14-6809","mag":"2250849282"},"language":"en","primary_location":{"id":"doi:10.3115/v1/w14-6809","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/w14-6809","pdf_url":"https://aclanthology.org/W14-6809.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Third CIPS-SIGHAN Joint Conference on Chinese Language Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/W14-6809.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014013223","display_name":"Odbal Odbal","orcid":null},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Odbal","raw_affiliation_strings":["Univ. of Sci. and Tech. of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Sci. and Tech. of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066334922","display_name":"Zengfu Wang","orcid":"https://orcid.org/0000-0003-0424-3010"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zengfu Wang","raw_affiliation_strings":["Univ. of Sci. and Tech. of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Sci. and Tech. of China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"52","last_page":"60"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9997000098228455,"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":0.9997000098228455,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.989799976348877,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9851999878883362,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7990580797195435},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.6997367143630981},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5953172445297241},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5922714471817017},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.5574531555175781},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5134742856025696},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5058262348175049},{"id":"https://openalex.org/keywords/emotion-detection","display_name":"Emotion detection","score":0.4662771224975586},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.242917001247406}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7990580797195435},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.6997367143630981},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5953172445297241},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5922714471817017},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5574531555175781},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5134742856025696},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5058262348175049},{"id":"https://openalex.org/C2988148770","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion detection","level":3,"score":0.4662771224975586},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.242917001247406},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3115/v1/w14-6809","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/w14-6809","pdf_url":"https://aclanthology.org/W14-6809.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Third CIPS-SIGHAN Joint Conference on Chinese Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.3115/v1/w14-6809","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/w14-6809","pdf_url":"https://aclanthology.org/W14-6809.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Third CIPS-SIGHAN Joint Conference on Chinese Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6899999976158142,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W2250849282.pdf"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W24085112","https://openalex.org/W25301398","https://openalex.org/W71795751","https://openalex.org/W104683736","https://openalex.org/W135937222","https://openalex.org/W810475488","https://openalex.org/W1513398909","https://openalex.org/W1675450783","https://openalex.org/W1889268436","https://openalex.org/W2102301788","https://openalex.org/W2103251029","https://openalex.org/W2104090402","https://openalex.org/W2125199674","https://openalex.org/W2130581319","https://openalex.org/W2132166724","https://openalex.org/W2150362751","https://openalex.org/W2158188757","https://openalex.org/W2160052288","https://openalex.org/W2168493061","https://openalex.org/W2172840424","https://openalex.org/W2250843958","https://openalex.org/W2251374610","https://openalex.org/W2404480901"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703"],"abstract_inverted_index":{"Emotion":[0],"detection":[1,39,89],"has":[2,44],"been":[3,45],"extensively":[4],"studied":[5],"in":[6,27,47,75],"recent":[7],"years.":[8],"Current":[9],"baseline":[10],"methods":[11],"often":[12],"use":[13],"token-based":[14],"features":[15],"which":[16],"cannot":[17],"properly":[18],"capture":[19],"more":[20],"complex":[21],"linguistic":[22],"phenomena":[23],"and":[24,67,97,100],"emotional":[25,73],"composition":[26,74],"fine":[28],"grained":[29],"emotion":[30,38,88],"detection.":[31],"A":[32],"novel":[33],"supervised":[34],"learning":[35],"approach\u2015segment-based":[36],"fine-grained":[37],"model":[40,57],"for":[41],"Chinese":[42],"text":[43,77],"proposed":[46,56,103,117],"this":[48],"paper.":[49],"Different":[50],"from":[51],"most":[52],"existing":[53,107],"methods,":[54],"the":[55,59,72,82,113,116],"applies":[58],"hierarchical":[60],"structure":[61],"of":[62,115],"sentence":[63],"(e.g.,":[64],"dependency":[65],"relationship)":[66],"exploits":[68],"segment-based":[69,118],"features.":[70],"Furthermore,":[71],"short":[76],"is":[78],"addressed":[79],"by":[80],"using":[81],"log":[83],"linear":[84],"model.":[85,119],"We":[86],"perform":[87],"on":[90],"our":[91,102],"dataset:":[92],"news":[93],"contents,":[94],"fairly":[95],"tales,":[96],"blog":[98],"dataset,":[99],"compare":[101],"method":[104],"to":[105],"representative":[106],"approaches.":[108],"The":[109],"experimental":[110],"results":[111],"demonstrate":[112],"effectiveness":[114]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
