{"id":"https://openalex.org/W2897299086","doi":"https://doi.org/10.1145/3269206.3269325","title":"Imbalanced Sentiment Classification with Multi-Task Learning","display_name":"Imbalanced Sentiment Classification with Multi-Task Learning","publication_year":2018,"publication_date":"2018-10-17","ids":{"openalex":"https://openalex.org/W2897299086","doi":"https://doi.org/10.1145/3269206.3269325","mag":"2897299086"},"language":"en","primary_location":{"id":"doi:10.1145/3269206.3269325","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3269206.3269325","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM International 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/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangzhao Wu","raw_affiliation_strings":["Microsoft Research Asia, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001967239","display_name":"Chuhan Wu","orcid":"https://orcid.org/0000-0001-5730-8792"},"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":"Chuhan 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":"last","author":{"id":"https://openalex.org/A5068189045","display_name":"Junxin Liu","orcid":"https://orcid.org/0009-0000-6634-4031"},"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":"Junxin Liu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9055,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.80509771,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1631","last_page":"1634"},"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.998199999332428,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.8087365031242371},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.7374258041381836},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7151938676834106},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6702700853347778},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.664749026298523},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6520081758499146},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5020632743835449},{"id":"https://openalex.org/keywords/multi-label-classification","display_name":"Multi-label classification","score":0.4949755072593689},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4648273289203644}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8087365031242371},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7374258041381836},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7151938676834106},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6702700853347778},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.664749026298523},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6520081758499146},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5020632743835449},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.4949755072593689},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4648273289203644},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3269206.3269325","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3269206.3269325","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W5236451","https://openalex.org/W1988824081","https://openalex.org/W2104167780","https://openalex.org/W2108646579","https://openalex.org/W2118099552","https://openalex.org/W2118978333","https://openalex.org/W2119191234","https://openalex.org/W2125069507","https://openalex.org/W2131744502","https://openalex.org/W2135046866","https://openalex.org/W2148143831","https://openalex.org/W2151732775","https://openalex.org/W2160660844","https://openalex.org/W2163302275","https://openalex.org/W2199803028","https://openalex.org/W2514769532","https://openalex.org/W2534255415","https://openalex.org/W2568951855","https://openalex.org/W2787893582","https://openalex.org/W4211186029","https://openalex.org/W6726302762"],"related_works":["https://openalex.org/W4292388283","https://openalex.org/W3204418343","https://openalex.org/W1560624709","https://openalex.org/W3166286441","https://openalex.org/W3214142563","https://openalex.org/W3111760155","https://openalex.org/W1998660429","https://openalex.org/W3091880950","https://openalex.org/W2157552837","https://openalex.org/W2783129721"],"abstract_inverted_index":{"Supervised":[0],"learning":[1,49,88],"methods":[2,19],"are":[3,39],"widely":[4],"used":[5],"in":[6,92,106],"sentiment":[7,11,31,57,70,76,90,109],"classification.":[8,32,110],"However,":[9],"when":[10],"distribution":[12],"is":[13,52],"imbalanced,":[14],"the":[15,42,87,101],"performance":[16],"of":[17,69,89,103],"these":[18,60],"declines.":[20],"In":[21,33,63],"this":[22],"paper,":[23],"we":[24,65],"propose":[25],"an":[26],"effective":[27],"approach":[28,84,105],"for":[29],"imbalanced":[30,43,93,108],"our":[34,83,104],"approach,":[35],"multiple":[36],"balanced":[37],"subsets":[38,61],"sampled":[40],"from":[41,59,73],"training":[44],"data":[45,81],"and":[46,78],"a":[47],"multi-task":[48],"based":[50],"framework":[51],"proposed":[53],"to":[54,85],"learn":[55],"robust":[56],"classifier":[58,91],"collaboratively.":[62],"addition,":[64],"incorporate":[66],"prior":[67],"knowledge":[68],"expressions":[71],"extracted":[72],"both":[74],"existing":[75],"lexicons":[77],"massive":[79],"unlabeled":[80],"into":[82],"enhance":[86],"scenario.":[94],"Experimental":[95],"results":[96],"on":[97],"benchmark":[98],"datasets":[99],"validate":[100],"effectiveness":[102],"improving":[107]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}