{"id":"https://openalex.org/W2741115544","doi":"https://doi.org/10.1145/3077136.3080693","title":"Sentence-level Sentiment Classification with Weak Supervision","display_name":"Sentence-level Sentiment Classification with Weak Supervision","publication_year":2017,"publication_date":"2017-07-28","ids":{"openalex":"https://openalex.org/W2741115544","doi":"https://doi.org/10.1145/3077136.3080693","mag":"2741115544"},"language":"en","primary_location":{"id":"doi:10.1145/3077136.3080693","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3077136.3080693","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval","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/A5031183808","display_name":"Jia Zhang","orcid":"https://orcid.org/0000-0003-0943-7543"},"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":"Jia Zhang","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/A5003505338","display_name":"Zhigang Yuan","orcid":"https://orcid.org/0000-0002-9299-2240"},"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":"Zhigang Yuan","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":"last","author":{"id":"https://openalex.org/A5030330619","display_name":"Jun Yan","orcid":"https://orcid.org/0000-0003-2497-5518"},"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":"Jun Yan","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"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.0143,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.95833924,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"973","last_page":"976"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":1.0,"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":1.0,"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":0.9984999895095825,"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":0.9962999820709229,"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/sentence","display_name":"Sentence","score":0.8450759649276733},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8208307027816772},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.7495235800743103},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7246790528297424},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7016246318817139},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6654061079025269},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5425305366516113},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.530299186706543},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.42144647240638733},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.10548052191734314}],"concepts":[{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.8450759649276733},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8208307027816772},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7495235800743103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7246790528297424},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7016246318817139},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6654061079025269},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5425305366516113},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.530299186706543},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.42144647240638733},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.10548052191734314},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3077136.3080693","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3077136.3080693","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.6299999952316284,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G2686420957","display_name":null,"funder_award_id":"U1536201, U1536207, 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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W193524605","https://openalex.org/W1832693441","https://openalex.org/W1989060270","https://openalex.org/W1991873845","https://openalex.org/W2098018055","https://openalex.org/W2108646579","https://openalex.org/W2125029327","https://openalex.org/W2131744502","https://openalex.org/W2156953626","https://openalex.org/W2160660844","https://openalex.org/W2163302275","https://openalex.org/W2250981850","https://openalex.org/W2251939518","https://openalex.org/W2252215182","https://openalex.org/W2577521916","https://openalex.org/W2787893582","https://openalex.org/W3101782091","https://openalex.org/W4211186029"],"related_works":["https://openalex.org/W2361861616","https://openalex.org/W2263699433","https://openalex.org/W2377979023","https://openalex.org/W2218034408","https://openalex.org/W2392921965","https://openalex.org/W2358755282","https://openalex.org/W3089396779","https://openalex.org/W2625833328","https://openalex.org/W2548633793","https://openalex.org/W3013279174"],"abstract_inverted_index":{"Sentence-level":[0],"sentiment":[1,14,28,48,74,80,105,122],"classification":[2,15,49],"is":[3,24,35,95],"important":[4],"to":[5,26,63,76,100],"understand":[6],"users'":[7],"fine-grained":[8],"opinions.":[9],"Existing":[10],"methods":[11],"for":[12,46],"sentence-level":[13,47,79,121],"are":[16],"mainly":[17],"based":[18],"on":[19,108],"supervised":[20],"learning.":[21],"However,":[22],"it":[23],"difficult":[25],"obtain":[27],"labels":[29],"of":[30,53,67,87,104,120],"sentences":[31,88,94],"since":[32],"manual":[33],"annotation":[34],"expensive":[36],"and":[37,72,89],"time-consuming.":[38],"In":[39,82],"this":[40],"paper,":[41],"we":[42,58],"propose":[43,59],"an":[44],"approach":[45,99,114],"without":[50],"the":[51,78,84,102,118],"need":[52],"sentence":[54],"labels.":[55],"More":[56],"specifically,":[57],"a":[60],"unified":[61],"framework":[62],"incorporate":[64],"two":[65],"types":[66],"weak":[68],"supervision,":[69],"i.e.,":[70],"document-level":[71],"word-level":[73],"labels,":[75],"learn":[77],"classifier.":[81,106],"addition,":[83],"contextual":[85],"information":[86],"words":[90],"extracted":[91],"from":[92],"unlabeled":[93],"incorporated":[96],"into":[97],"our":[98,113],"enhance":[101],"learning":[103],"Experiments":[107],"benchmark":[109],"datasets":[110],"show":[111],"that":[112],"can":[115],"effectively":[116],"improve":[117],"performance":[119],"classification.":[123]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":22},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}