{"id":"https://openalex.org/W2533608434","doi":"https://doi.org/10.1145/2983323.2983729","title":"Generative Feature Language Models for Mining Implicit Features from Customer Reviews","display_name":"Generative Feature Language Models for Mining Implicit Features from Customer Reviews","publication_year":2016,"publication_date":"2016-10-24","ids":{"openalex":"https://openalex.org/W2533608434","doi":"https://doi.org/10.1145/2983323.2983729","mag":"2533608434"},"language":"en","primary_location":{"id":"doi:10.1145/2983323.2983729","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2983323.2983729","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":"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/A5058755530","display_name":"Shubhra Kanti Karmaker Santu","orcid":null},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shubhra Kanti Karmaker Santu","raw_affiliation_strings":["Univeristy of Illinois at Urbana-Champaign, Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"Univeristy of Illinois at Urbana-Champaign, Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001811638","display_name":"Parikshit Sondhi","orcid":null},"institutions":[{"id":"https://openalex.org/I1330693074","display_name":"Walmart (United States)","ror":"https://ror.org/04j0gge90","country_code":"US","type":"company","lineage":["https://openalex.org/I1330693074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Parikshit Sondhi","raw_affiliation_strings":["WalmartLabs, Sunnyvale, CA, USA"],"affiliations":[{"raw_affiliation_string":"WalmartLabs, Sunnyvale, CA, USA","institution_ids":["https://openalex.org/I1330693074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028518494","display_name":"ChengXiang Zhai","orcid":"https://orcid.org/0000-0002-6434-3702"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"ChengXiang Zhai","raw_affiliation_strings":["Univeristy of Illinois at Urbana-Champaign, Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"Univeristy of Illinois at Urbana-Champaign, Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5058755530"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":2.9989,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.92942502,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"929","last_page":"938"},"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9993000030517578,"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":0.9987999796867371,"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/computer-science","display_name":"Computer science","score":0.8650180101394653},{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.8534232378005981},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6443645358085632},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5924084782600403},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.5667785406112671},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5556042790412903},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5005862712860107},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5004227161407471},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.49749425053596497},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.48633939027786255},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4676365852355957},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.42469531297683716},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.4103281795978546},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3679649233818054}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8650180101394653},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.8534232378005981},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6443645358085632},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5924084782600403},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.5667785406112671},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5556042790412903},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5005862712860107},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5004227161407471},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.49749425053596497},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.48633939027786255},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4676365852355957},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.42469531297683716},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.4103281795978546},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3679649233818054},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2983323.2983729","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2983323.2983729","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":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W38739846","https://openalex.org/W82110502","https://openalex.org/W644717764","https://openalex.org/W1515087027","https://openalex.org/W1544805753","https://openalex.org/W1612003148","https://openalex.org/W1880262756","https://openalex.org/W1991418309","https://openalex.org/W2022204871","https://openalex.org/W2049633694","https://openalex.org/W2070694721","https://openalex.org/W2081375810","https://openalex.org/W2112744748","https://openalex.org/W2114581066","https://openalex.org/W2129294185","https://openalex.org/W2141631351","https://openalex.org/W2158698691","https://openalex.org/W2160660844","https://openalex.org/W4205184193","https://openalex.org/W6620943897","https://openalex.org/W6678923525","https://openalex.org/W7048738093"],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W4395044357","https://openalex.org/W4287117424","https://openalex.org/W4387506531","https://openalex.org/W2087346071","https://openalex.org/W2967848559","https://openalex.org/W4299831724"],"abstract_inverted_index":{"Online":[0],"customer":[1],"reviews":[2],"are":[3,86,131],"very":[4,161],"useful":[5],"for":[6,26,44,163],"both":[7],"helping":[8],"consumers":[9],"make":[10],"buying":[11],"decisions":[12],"on":[13,112],"products":[14],"or":[15,91],"services":[16],"and":[17,48,57,85,175],"providing":[18],"business":[19],"intelligence.":[20],"However,":[21],"it":[22],"is":[23,59,160],"a":[24,73,108,181],"challenge":[25,52],"people":[27],"to":[28,60,89,145,166],"manually":[29],"digest":[30],"all":[31],"the":[32,42,66,120,173,177],"opinions":[33],"buried":[34],"in":[35,53,72,151],"large":[36,182],"amounts":[37],"of":[38,148],"review":[39,74,101],"data,":[40],"raising":[41],"need":[43],"automatic":[45,54],"opinion":[46,55],"summarization":[47,56],"analysis.":[49],"One":[50],"fundamental":[51],"analysis":[58],"mine":[61,119],"implicit":[62,121],"features,":[63,174],"i.e.,":[64],"recognizing":[65],"features":[67,122,165],"implicitly":[68],"mentioned":[69],"(referred":[70],"to)":[71],"sentence.":[75],"Existing":[76],"approaches":[77],"require":[78],"many":[79],"ad":[80],"hoc":[81],"manual":[82],"parameter":[83],"tuning,":[84],"thus":[87],"hard":[88],"optimize":[90],"generalize;":[92],"their":[93],"evaluation":[94,147],"has":[95],"only":[96],"been":[97],"done":[98],"with":[99],"Chinese":[100],"data.":[102],"In":[103],"this":[104,149],"paper,":[105],"we":[106],"propose":[107],"new":[109,142],"approach":[110,159],"based":[111],"generative":[113],"feature":[114],"language":[115],"models":[116],"that":[117,156,168],"can":[118],"more":[123],"effectively":[124],"through":[125],"unsupervised":[126],"statistical":[127],"learning.":[128],"The":[129],"parameters":[130],"optimized":[132],"automatically":[133],"using":[134],"an":[135],"Expectation-Maximization":[136],"algorithm.":[137],"We":[138],"also":[139],"created":[140],"eight":[141],"data":[143],"sets":[144],"facilitate":[146],"task":[150],"English.":[152],"Experimental":[153],"results":[154],"show":[155],"our":[157],"proposed":[158],"effective":[162],"assigning":[164],"sentences":[167],"do":[169],"not":[170],"explicitly":[171],"mention":[172],"outperforms":[176],"existing":[178],"algorithms":[179],"by":[180],"margin.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
