{"id":"https://openalex.org/W2534255415","doi":"https://doi.org/10.1145/2983323.2983851","title":"Sentiment Domain Adaptation with Multi-Level Contextual Sentiment Knowledge","display_name":"Sentiment Domain Adaptation with Multi-Level Contextual Sentiment Knowledge","publication_year":2016,"publication_date":"2016-10-24","ids":{"openalex":"https://openalex.org/W2534255415","doi":"https://doi.org/10.1145/2983323.2983851","mag":"2534255415"},"language":"en","primary_location":{"id":"doi:10.1145/2983323.2983851","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2983323.2983851","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"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/A5076423724","display_name":"Fangzhao Wu","orcid":"https://orcid.org/0000-0001-9138-1272"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangzhao Wu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076051057","display_name":"Sixing Wu","orcid":"https://orcid.org/0009-0008-3024-0802"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sixing Wu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100768896","display_name":"Yongfeng Huang","orcid":"https://orcid.org/0000-0003-3825-2230"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongfeng Huang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047856952","display_name":"Songfang Huang","orcid":"https://orcid.org/0000-0001-8084-0904"},"institutions":[{"id":"https://openalex.org/I4210126794","display_name":"IBM Research (China)","ror":"https://ror.org/02yg1pf55","country_code":"CN","type":"company","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126794"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Songfang Huang","raw_affiliation_strings":["IBM Research - China, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research - China, Beijing, China","institution_ids":["https://openalex.org/I4210126794"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088716214","display_name":"Yong Qin","orcid":"https://orcid.org/0000-0002-6519-8316"},"institutions":[{"id":"https://openalex.org/I4210126794","display_name":"IBM Research (China)","ror":"https://ror.org/02yg1pf55","country_code":"CN","type":"company","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126794"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Qin","raw_affiliation_strings":["IBM Research - China, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research - China, Beijing, China","institution_ids":["https://openalex.org/I4210126794"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"949","last_page":"958"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998999834060669,"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":0.9998999834060669,"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/T10028","display_name":"Topic Modeling","score":0.996999979019165,"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":0.9957000017166138,"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/sentiment-analysis","display_name":"Sentiment analysis","score":0.9014747142791748},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8407901525497437},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7382246255874634},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.7095402479171753},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6802980899810791},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6135836839675903},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5876385569572449},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4112524390220642},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.39740434288978577},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06791040301322937}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.9014747142791748},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8407901525497437},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7382246255874634},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.7095402479171753},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6802980899810791},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6135836839675903},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5876385569572449},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4112524390220642},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.39740434288978577},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06791040301322937},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2983323.2983851","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2983323.2983851","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5876828520","display_name":null,"funder_award_id":"U1536201, U1405254","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W22861983","https://openalex.org/W38739846","https://openalex.org/W359818833","https://openalex.org/W1989060270","https://openalex.org/W2022204871","https://openalex.org/W2043270411","https://openalex.org/W2068599126","https://openalex.org/W2070824330","https://openalex.org/W2094755907","https://openalex.org/W2097089247","https://openalex.org/W2100556411","https://openalex.org/W2107008379","https://openalex.org/W2108646579","https://openalex.org/W2122369144","https://openalex.org/W2122825543","https://openalex.org/W2135046866","https://openalex.org/W2136680862","https://openalex.org/W2151262965","https://openalex.org/W2151752537","https://openalex.org/W2153353890","https://openalex.org/W2155328222","https://openalex.org/W2158536343","https://openalex.org/W2160660844","https://openalex.org/W2163302275","https://openalex.org/W2164278908","https://openalex.org/W2165698076","https://openalex.org/W2166706824","https://openalex.org/W2199803028","https://openalex.org/W2218755335","https://openalex.org/W2248051232","https://openalex.org/W2296319761","https://openalex.org/W2346975490","https://openalex.org/W2396578736","https://openalex.org/W2397710600","https://openalex.org/W2417677256","https://openalex.org/W2514769532","https://openalex.org/W2568951855","https://openalex.org/W3101782091","https://openalex.org/W3146306708","https://openalex.org/W4205184193","https://openalex.org/W4211186029","https://openalex.org/W4244393449","https://openalex.org/W4292363360"],"related_works":["https://openalex.org/W3080655457","https://openalex.org/W4389474468","https://openalex.org/W3166286441","https://openalex.org/W3214142563","https://openalex.org/W3136267388","https://openalex.org/W3186065094","https://openalex.org/W4287263085","https://openalex.org/W3093803318","https://openalex.org/W3204418343","https://openalex.org/W4300172004"],"abstract_inverted_index":{"Sentiment":[0],"domain":[1,16,27,34,68,132],"adaptation":[2,17,55,69],"is":[3],"widely":[4],"studied":[5],"to":[6,31,80,118,152,163,188],"tackle":[7],"the":[8,32,41,46,54,73,85,106,145,190,201],"domain-dependence":[9],"problem":[10],"in":[11,24,76,97,110,130,148],"sentiment":[12,22,42,67,74,78,87,91,107,124,137,140,146,150,155,169,176],"analysis":[13],"field.":[14],"Existing":[15],"methods":[18],"usually":[19,93],"train":[20],"a":[21,25,65,81,111,160],"classifier":[23,108,177],"source":[26,47,112],"and":[28,48,133,171,203],"adapt":[29],"it":[30],"target":[33,49,131,179],"using":[35],"transfer":[36],"learning":[37],"techniques.":[38],"However,":[39],"when":[40],"feature":[43],"distributions":[44],"of":[45,89,122,168,192,205],"domains":[50],"are":[51],"significantly":[52],"different,":[53],"performance":[56,104],"will":[57],"heavily":[58],"decline.":[59],"In":[60,114],"this":[61],"paper,":[62],"we":[63,116,158,182],"propose":[64,117,159,183],"new":[66],"approach":[70],"by":[71],"adapting":[72],"knowledge":[75,125,170],"general-purpose":[77,90],"lexicons":[79,92],"specific":[82],"domain.":[83,113,180],"Since":[84],"general":[86,149],"words":[88,151],"convey":[94],"consistent":[95],"sentiments":[96],"different":[98,166],"domains,":[99],"they":[100],"have":[101],"better":[102],"generalization":[103],"than":[105],"trained":[109],"addition,":[115],"extract":[119],"various":[120],"kinds":[121,167],"contextual":[123],"from":[126],"massive":[127,153],"unlabeled":[128],"samples":[129],"formulate":[134],"them":[135],"as":[136],"relations":[138],"among":[139],"expressions.":[141,156],"It":[142],"can":[143],"propagate":[144],"information":[147],"domain-specific":[154,175],"Besides,":[157],"unified":[161],"framework":[162],"incorporate":[164],"these":[165],"learn":[172],"an":[173,184],"accurate":[174],"for":[178],"Moreover,":[181],"efficient":[185],"optimization":[186],"algorithm":[187],"solve":[189],"model":[191],"our":[193,206],"approach.":[194,207],"Extensive":[195],"experiments":[196],"on":[197],"benchmark":[198],"datasets":[199],"validate":[200],"effectiveness":[202],"efficiency":[204]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}