{"id":"https://openalex.org/W2248676660","doi":"https://doi.org/10.1109/tkde.2015.2475761","title":"Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings","display_name":"Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings","publication_year":2015,"publication_date":"2015-09-02","ids":{"openalex":"https://openalex.org/W2248676660","doi":"https://doi.org/10.1109/tkde.2015.2475761","mag":"2248676660"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2015.2475761","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2015.2475761","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","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/A5073503574","display_name":"Danushka Bollegala","orcid":"https://orcid.org/0000-0003-4476-7003"},"institutions":[{"id":"https://openalex.org/I146655781","display_name":"University of Liverpool","ror":"https://ror.org/04xs57h96","country_code":"GB","type":"education","lineage":["https://openalex.org/I146655781"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Danushka Bollegala","raw_affiliation_strings":["School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, Brownlow Hill, Liverpool, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, Brownlow Hill, Liverpool, United Kingdom","institution_ids":["https://openalex.org/I146655781"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048659336","display_name":"Tingting Mu","orcid":"https://orcid.org/0000-0001-6315-3432"},"institutions":[{"id":"https://openalex.org/I146655781","display_name":"University of Liverpool","ror":"https://ror.org/04xs57h96","country_code":"GB","type":"education","lineage":["https://openalex.org/I146655781"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Tingting Mu","raw_affiliation_strings":["School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, Brownlow Hill, Liverpool, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, Brownlow Hill, Liverpool, United Kingdom","institution_ids":["https://openalex.org/I146655781"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079825375","display_name":"John Y. Goulermas","orcid":"https://orcid.org/0000-0003-0381-124X"},"institutions":[{"id":"https://openalex.org/I146655781","display_name":"University of Liverpool","ror":"https://ror.org/04xs57h96","country_code":"GB","type":"education","lineage":["https://openalex.org/I146655781"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"John Yannis Goulermas","raw_affiliation_strings":["School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, Brownlow Hill, Liverpool, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, Brownlow Hill, Liverpool, United Kingdom","institution_ids":["https://openalex.org/I146655781"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I146655781"],"apc_list":null,"apc_paid":null,"fwci":19.644,"has_fulltext":false,"cited_by_count":109,"citation_normalized_percentile":{"value":0.99332614,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":100},"biblio":{"volume":"28","issue":"2","first_page":"398","last_page":"410"},"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9991999864578247,"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":0.9972000122070312,"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/classifier","display_name":"Classifier (UML)","score":0.782738208770752},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7758702635765076},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.700419545173645},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.678419291973114},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5870643854141235},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5822957158088684},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5603475570678711},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5034064650535583},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.419332355260849},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3865293860435486},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32968926429748535},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11579078435897827}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.782738208770752},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7758702635765076},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.700419545173645},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.678419291973114},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5870643854141235},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5822957158088684},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5603475570678711},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5034064650535583},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.419332355260849},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3865293860435486},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32968926429748535},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11579078435897827},{"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":3,"locations":[{"id":"doi:10.1109/tkde.2015.2475761","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2015.2475761","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","raw_type":"journal-article"},{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/4a66a6ad-246c-41ee-a162-c47d3b745ab0","is_oa":false,"landing_page_url":"https://research.manchester.ac.uk/en/publications/4a66a6ad-246c-41ee-a162-c47d3b745ab0","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bollegala, D, Mu, T & Goulermas, J Y 2015, 'Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings', IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 28, no. 2, pp. 398-410. https://doi.org/10.1109/TKDE.2015.2475761","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:pure.atira.dk:publications/4a66a6ad-246c-41ee-a162-c47d3b745ab0","is_oa":false,"landing_page_url":"https://www.research.manchester.ac.uk/portal/en/publications/crossdomain-sentiment-classification-using-sentiment-sensitive-embeddings(4a66a6ad-246c-41ee-a162-c47d3b745ab0).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Bollegala, D, Mu, T & Goulermas, J Y 2015, 'Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings', IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 28, no. 2, pp. 398-410. https://doi.org/10.1109/TKDE.2015.2475761","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W22861983","https://openalex.org/W1574901103","https://openalex.org/W1969204685","https://openalex.org/W2001619934","https://openalex.org/W2006475033","https://openalex.org/W2022286021","https://openalex.org/W2026164260","https://openalex.org/W2029181554","https://openalex.org/W2045812729","https://openalex.org/W2095579012","https://openalex.org/W2097726431","https://openalex.org/W2104094955","https://openalex.org/W2109531142","https://openalex.org/W2111427896","https://openalex.org/W2117756735","https://openalex.org/W2120354757","https://openalex.org/W2123160630","https://openalex.org/W2125778906","https://openalex.org/W2132914434","https://openalex.org/W2137404238","https://openalex.org/W2141599568","https://openalex.org/W2141671708","https://openalex.org/W2145071407","https://openalex.org/W2147152072","https://openalex.org/W2148694408","https://openalex.org/W2153353890","https://openalex.org/W2153579005","https://openalex.org/W2154872931","https://openalex.org/W2158108973","https://openalex.org/W2158751697","https://openalex.org/W2160660844","https://openalex.org/W2163302275","https://openalex.org/W2164384659","https://openalex.org/W2165698076","https://openalex.org/W2166706824","https://openalex.org/W2798909945","https://openalex.org/W2951278869","https://openalex.org/W3014771378","https://openalex.org/W3041093287","https://openalex.org/W4205184193","https://openalex.org/W4292023222","https://openalex.org/W4294170691","https://openalex.org/W6600949241","https://openalex.org/W6676189307","https://openalex.org/W6678460774","https://openalex.org/W6680611266","https://openalex.org/W6680890276","https://openalex.org/W6682644385","https://openalex.org/W6682691769","https://openalex.org/W6684149856","https://openalex.org/W6763745640"],"related_works":["https://openalex.org/W2361861616","https://openalex.org/W2263699433","https://openalex.org/W2377979023","https://openalex.org/W2218034408","https://openalex.org/W4292388283","https://openalex.org/W3204418343","https://openalex.org/W1560624709","https://openalex.org/W3166286441","https://openalex.org/W3214142563","https://openalex.org/W3111760155"],"abstract_inverted_index":{"Unsupervised":[0],"Cross-domain":[1],"Sentiment":[2],"Classification":[3],"is":[4,204],"the":[5,39,56,63,103,112,132,168,186,197,201,209,222],"task":[6],"of":[7,83,131,188],"adapting":[8,43],"a":[9,14,24,127,139,160],"sentiment":[10,46,135,140,155,194,214,230],"classifier":[11,47,141],"trained":[12],"on":[13,159],"particular":[15],"domain":[16,26,105,134],"(":[17,27],"<italic":[18,28,84],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[19,29,85],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">source":[20],"domain</i>":[21,31],"),":[22,32],"to":[23,48,154,178,221],"different":[25],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">target":[30],"without":[33],"requiring":[34],"any":[35],"labeled":[36],"data":[37,60],"for":[38,58,62,192,228],"target":[40,51,64,97,119],"domain.":[41,65],"By":[42],"an":[44],"existing":[45],"previously":[49],"unseen":[50],"domains,":[52],"we":[53,171],"can":[54,172],"avoid":[55],"cost":[57],"manual":[59],"annotation":[61],"We":[66],"model":[67],"this":[68,143],"problem":[69],"as":[70],"embedding":[71,129,190,225],"learning,":[72],"and":[73,96,107,118,137],"construct":[74],"three":[75,169],"objective":[76,181,199],"functions":[77],"that":[78,91,124,151,164,217],"capture:":[79],"(a)":[80],"distributional":[81],"properties":[82,110],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">":[86],"pivots</i>":[87],"(i.e.,":[88],"common":[89],"features":[90],"appear":[92],"in":[93,102,111,115,142,176],"both":[94,116],"source":[95,104,117,133],"domains),":[98],"(b)":[99],"label":[100],"constraints":[101],"documents,":[106],"(c)":[108],"geometric":[109],"unlabeled":[113],"documents":[114],"domains.":[120],"Unlike":[121],"prior":[122],"proposals":[123],"first":[125],"learn":[126],"lower-dimensional":[128],"independent":[130],"labels,":[136],"next":[138],"embedding,":[144],"our":[145],"joint":[146],"optimisation":[147],"method":[148,211],"learns":[149],"embeddings":[150],"are":[152,218],"sensitive":[153],"classification.":[156,195,231],"Experimental":[157],"results":[158],"benchmark":[161],"dataset":[162],"show":[163],"by":[165,206],"jointly":[166],"optimising":[167,179],"objectives":[170],"obtain":[173],"better":[174],"performances":[175],"comparison":[177],"each":[180],"function":[182],"separately,":[183],"thereby":[184],"demonstrating":[185],"importance":[187],"task-specific":[189],"learning":[191,226],"cross-domain":[193,213,229],"Among":[196],"individual":[198],"functions,":[200],"best":[202],"performance":[203],"obtained":[205],"(c).":[207],"Moreover,":[208],"proposed":[210],"reports":[212],"classification":[215],"accuracies":[216],"statistically":[219],"comparable":[220],"current":[223],"state-of-the-art":[224],"methods":[227]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":17},{"year":2018,"cited_by_count":21},{"year":2017,"cited_by_count":21},{"year":2016,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
