{"id":"https://openalex.org/W4225783786","doi":"https://doi.org/10.1109/taffc.2022.3158843","title":"Counterfactual Representation Augmentation for Cross-Domain Sentiment Analysis","display_name":"Counterfactual Representation Augmentation for Cross-Domain Sentiment Analysis","publication_year":2022,"publication_date":"2022-03-15","ids":{"openalex":"https://openalex.org/W4225783786","doi":"https://doi.org/10.1109/taffc.2022.3158843"},"language":"en","primary_location":{"id":"doi:10.1109/taffc.2022.3158843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2022.3158843","pdf_url":null,"source":{"id":"https://openalex.org/S104780363","display_name":"IEEE Transactions on Affective Computing","issn_l":"1949-3045","issn":["1949-3045","2371-9850"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Affective Computing","raw_type":"journal-article"},"type":"article","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/A5101445828","display_name":"Ke Wang","orcid":"https://orcid.org/0000-0003-2300-0743"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Wang","raw_affiliation_strings":["Wangxuan Institute of Computer Technology, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2300-0743","affiliations":[{"raw_affiliation_string":"Wangxuan Institute of Computer Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029568096","display_name":"Xiaojun Wan","orcid":"https://orcid.org/0000-0001-6887-1994"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojun Wan","raw_affiliation_strings":["Wangxuan Institute of Computer Technology, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6887-1994","affiliations":[{"raw_affiliation_string":"Wangxuan Institute of Computer Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":null,"fwci":1.0248,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.79764488,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"14","issue":"3","first_page":"1979","last_page":"1990"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9994999766349792,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.7670759558677673},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.7445602416992188},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7316656112670898},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7146337032318115},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6543923616409302},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6353282928466797},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.5767138004302979},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5212700366973877},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5023167133331299},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.44059526920318604},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4157341718673706},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3572987914085388},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1649194061756134},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.07341450452804565}],"concepts":[{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.7670759558677673},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.7445602416992188},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7316656112670898},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7146337032318115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6543923616409302},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6353282928466797},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.5767138004302979},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5212700366973877},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5023167133331299},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.44059526920318604},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4157341718673706},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3572987914085388},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1649194061756134},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.07341450452804565},{"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taffc.2022.3158843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2022.3158843","pdf_url":null,"source":{"id":"https://openalex.org/S104780363","display_name":"IEEE Transactions on Affective Computing","issn_l":"1949-3045","issn":["1949-3045","2371-9850"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Affective Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7200000286102295}],"awards":[{"id":"https://openalex.org/G3544945854","display_name":"\u591a\u8bed\u8a00\u73af\u5883\u4e0b\u6587\u672c\u60c5\u611f\u8bed\u4e49\u8ba1\u7b97\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"61772036","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":135,"referenced_works":["https://openalex.org/W22861983","https://openalex.org/W91453536","https://openalex.org/W1731081199","https://openalex.org/W1882958252","https://openalex.org/W1931477419","https://openalex.org/W1945616565","https://openalex.org/W1977088242","https://openalex.org/W1978108654","https://openalex.org/W1985258161","https://openalex.org/W2055103902","https://openalex.org/W2068599126","https://openalex.org/W2095579012","https://openalex.org/W2096873754","https://openalex.org/W2097726431","https://openalex.org/W2099471337","https://openalex.org/W2108018401","https://openalex.org/W2115403315","https://openalex.org/W2116064496","https://openalex.org/W2118045473","https://openalex.org/W2124536510","https://openalex.org/W2143117649","https://openalex.org/W2143891888","https://openalex.org/W2151752537","https://openalex.org/W2153353890","https://openalex.org/W2163302275","https://openalex.org/W2167216307","https://openalex.org/W2187089797","https://openalex.org/W2248676660","https://openalex.org/W2333097151","https://openalex.org/W2471364849","https://openalex.org/W2551835155","https://openalex.org/W2560730294","https://openalex.org/W2561230197","https://openalex.org/W2573291455","https://openalex.org/W2575474854","https://openalex.org/W2605323333","https://openalex.org/W2612769033","https://openalex.org/W2741989495","https://openalex.org/W2753845591","https://openalex.org/W2767280341","https://openalex.org/W2789132801","https://openalex.org/W2798717312","https://openalex.org/W2798820405","https://openalex.org/W2798984401","https://openalex.org/W2803777992","https://openalex.org/W2889774592","https://openalex.org/W2890896513","https://openalex.org/W2896457183","https://openalex.org/W2918288435","https://openalex.org/W2919115771","https://openalex.org/W2945801692","https://openalex.org/W2947314843","https://openalex.org/W2947630374","https://openalex.org/W2950705418","https://openalex.org/W2950940239","https://openalex.org/W2951286828","https://openalex.org/W2952984539","https://openalex.org/W2962681511","https://openalex.org/W2962727366","https://openalex.org/W2962739339","https://openalex.org/W2962833164","https://openalex.org/W2962869292","https://openalex.org/W2963217615","https://openalex.org/W2963518342","https://openalex.org/W2963891433","https://openalex.org/W2963969878","https://openalex.org/W2964044490","https://openalex.org/W2964236337","https://openalex.org/W2965373594","https://openalex.org/W2970019270","https://openalex.org/W2970379526","https://openalex.org/W2970597249","https://openalex.org/W2977235550","https://openalex.org/W2980282514","https://openalex.org/W2984256198","https://openalex.org/W2985026420","https://openalex.org/W2995191098","https://openalex.org/W3005283300","https://openalex.org/W3025630664","https://openalex.org/W3035431747","https://openalex.org/W3089183975","https://openalex.org/W3106544837","https://openalex.org/W3144194608","https://openalex.org/W3152368098","https://openalex.org/W3160851104","https://openalex.org/W3176001432","https://openalex.org/W3176197839","https://openalex.org/W3199724653","https://openalex.org/W3208191520","https://openalex.org/W3211867455","https://openalex.org/W4239510810","https://openalex.org/W4240795372","https://openalex.org/W4285688121","https://openalex.org/W4287101239","https://openalex.org/W4288600473","https://openalex.org/W4288617013","https://openalex.org/W4289744728","https://openalex.org/W4295253939","https://openalex.org/W4300996741","https://openalex.org/W4385245566","https://openalex.org/W6600949241","https://openalex.org/W6610754620","https://openalex.org/W6637618735","https://openalex.org/W6639480849","https://openalex.org/W6640425456","https://openalex.org/W6640570924","https://openalex.org/W6674691379","https://openalex.org/W6674764686","https://openalex.org/W6675240855","https://openalex.org/W6676264761","https://openalex.org/W6677741084","https://openalex.org/W6682261130","https://openalex.org/W6684149856","https://openalex.org/W6730161283","https://openalex.org/W6730389898","https://openalex.org/W6732022357","https://openalex.org/W6734335776","https://openalex.org/W6739901393","https://openalex.org/W6744097617","https://openalex.org/W6744110554","https://openalex.org/W6753611882","https://openalex.org/W6755207826","https://openalex.org/W6757235116","https://openalex.org/W6758720947","https://openalex.org/W6759517920","https://openalex.org/W6762900673","https://openalex.org/W6763701032","https://openalex.org/W6766673545","https://openalex.org/W6768299147","https://openalex.org/W6768817161","https://openalex.org/W6770268497","https://openalex.org/W6775589563","https://openalex.org/W6783777954","https://openalex.org/W6793003553","https://openalex.org/W6798466024"],"related_works":["https://openalex.org/W3201448254","https://openalex.org/W4286970243","https://openalex.org/W4293202849","https://openalex.org/W1980965563","https://openalex.org/W1489300767","https://openalex.org/W2066431708","https://openalex.org/W4384133558","https://openalex.org/W2596763562","https://openalex.org/W2964218010","https://openalex.org/W2808862658"],"abstract_inverted_index":{"Cross-domain":[0],"sentiment":[1,6,139],"analysis":[2],"aims":[3,54],"to":[4,16,55,72,92,119,125,159,175],"adapt":[5],"classification":[7,140],"models":[8,39],"trained":[9],"on":[10,41,83,128,135,152],"one":[11],"or":[12],"more":[13,127,179],"source":[14,79,104],"domains":[15,82],"the":[17,24,74,78,99,103,108,122,156,172,177],"target":[18,81],"domain,":[19],"which":[20,53,97],"can":[21,169],"effectively":[22,170],"alleviate":[23],"problem":[25],"of":[26,37,102],"insufficient":[27],"labelled":[28],"data":[29],"in":[30],"specific":[31],"domains.":[32],"Unlike":[33],"most":[34],"previous":[35],"methods":[36,151],"adjusting":[38],"based":[40],"observed":[42],"data,":[43,85],"we":[44,67,112],"propose":[45],"a":[46,69,88,136],"novel":[47],"counterfactual":[48,62,95],"representation":[49],"augmentation":[50],"(CRA)":[51],"method,":[52],"improve":[56,160],"target-domain":[57,123],"generalization":[58],"by":[59],"constructing":[60],"new":[61],"representations":[63],"for":[64,131],"training.":[65],"Specifically,":[66],"train":[68],"domain":[70,75,105,161],"discriminator":[71],"learn":[73],"discrepancy":[76],"between":[77],"and":[80,86,106,181,186],"unlabeled":[84],"use":[87],"gradient":[89],"editing":[90],"method":[91,118,146],"directly":[93],"construct":[94],"representations,":[96],"reduces":[98],"inductive":[100],"bias":[101],"augments":[107],"training":[109,117],"data.":[110],"Moreover,":[111],"further":[113],"leverage":[114],"an":[115],"ensemble-based":[116],"indirectly":[120],"encourage":[121],"classifier":[124,178],"rely":[126],"robust":[129,180],"features":[130],"prediction.":[132],"Extensive":[133],"experiments":[134],"widely-used":[137],"cross-domain":[138],"benchmark":[141],"dataset":[142],"show":[143],"that":[144,166],"our":[145,167],"consistently":[147],"surpasses":[148],"different":[149,153],"baseline":[150],"tasks,":[154],"demonstrating":[155],"strong":[157],"ability":[158],"generalization.":[162],"We":[163],"also":[164],"find":[165],"model":[168],"adjust":[171],"decision":[173],"boundary":[174],"make":[176],"generalized,":[182],"through":[183],"extensive":[184],"qualitative":[185],"quantitative":[187],"analysis.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2025-10-10T00:00:00"}
