{"id":"https://openalex.org/W1967274749","doi":"https://doi.org/10.1145/2396761.2396863","title":"On the design of LDA models for aspect-based opinion mining","display_name":"On the design of LDA models for aspect-based opinion mining","publication_year":2012,"publication_date":"2012-10-29","ids":{"openalex":"https://openalex.org/W1967274749","doi":"https://doi.org/10.1145/2396761.2396863","mag":"1967274749"},"language":"en","primary_location":{"id":"doi:10.1145/2396761.2396863","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2396761.2396863","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21st ACM international 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/A5059571650","display_name":"Samaneh Moghaddam","orcid":null},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Samaneh Moghaddam","raw_affiliation_strings":["Simon Fraser University, Burnaby, BC, Canada","[Simon Fraser University Burnaby, BC, Canada]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Simon Fraser University, Burnaby, BC, Canada","institution_ids":["https://openalex.org/I18014758"]},{"raw_affiliation_string":"[Simon Fraser University Burnaby, BC, Canada]","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018267399","display_name":"Martin Ester","orcid":"https://orcid.org/0000-0001-7732-2815"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Martin Ester","raw_affiliation_strings":["Simon Fraser University, Burnaby, BC, Canada","[Simon Fraser University Burnaby, BC, Canada]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Simon Fraser University, Burnaby, BC, Canada","institution_ids":["https://openalex.org/I18014758"]},{"raw_affiliation_string":"[Simon Fraser University Burnaby, BC, Canada]","institution_ids":["https://openalex.org/I18014758"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":20.3451,"has_fulltext":false,"cited_by_count":133,"citation_normalized_percentile":{"value":0.99445528,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"803","last_page":"812"},"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.995199978351593,"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":0.98089998960495,"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/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.8969674110412598},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7579845786094666},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6678882837295532},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.6512411236763},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5915826559066772},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5267980694770813},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.49203822016716003},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47300368547439575},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46447718143463135},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3261255621910095}],"concepts":[{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.8969674110412598},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7579845786094666},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6678882837295532},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.6512411236763},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5915826559066772},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5267980694770813},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49203822016716003},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47300368547439575},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46447718143463135},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3261255621910095},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2396761.2396863","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2396761.2396863","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21st ACM international conference on Information and knowledge management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W89332836","https://openalex.org/W1508977358","https://openalex.org/W1515087027","https://openalex.org/W1536516100","https://openalex.org/W1581485226","https://openalex.org/W1859957297","https://openalex.org/W1880262756","https://openalex.org/W1964613733","https://openalex.org/W1971889796","https://openalex.org/W1999681713","https://openalex.org/W2001587475","https://openalex.org/W2007427105","https://openalex.org/W2019207508","https://openalex.org/W2044429219","https://openalex.org/W2047676437","https://openalex.org/W2096110600","https://openalex.org/W2108420397","https://openalex.org/W2113786470","https://openalex.org/W2121392694","https://openalex.org/W2129604374","https://openalex.org/W2137191349","https://openalex.org/W2141631351","https://openalex.org/W2145071407","https://openalex.org/W2151752537","https://openalex.org/W2152152130","https://openalex.org/W2154970197","https://openalex.org/W2160660844","https://openalex.org/W2162860143","https://openalex.org/W2404032054","https://openalex.org/W2519011775","https://openalex.org/W6639619044","https://openalex.org/W6674809819"],"related_works":["https://openalex.org/W2888805565","https://openalex.org/W4312773271","https://openalex.org/W4315588616","https://openalex.org/W2769501189","https://openalex.org/W2962686197","https://openalex.org/W2207653751","https://openalex.org/W4293863151","https://openalex.org/W3159709618","https://openalex.org/W2611137333","https://openalex.org/W3005513013"],"abstract_inverted_index":{"Aspect-based":[0],"opinion":[1,117],"mining,":[2],"which":[3],"aims":[4],"to":[5,21,37,72,144],"extract":[6],"aspects":[7],"and":[8,141,167,184,193],"their":[9],"corresponding":[10],"ratings":[11],"from":[12,65,163],"customers":[13,20],"reviews,":[14],"provides":[15],"very":[16,158],"useful":[17],"information":[18],"for":[19,115],"make":[22],"purchase":[23],"decisions.":[24,151],"In":[25,105],"the":[26,80,83,96,134,137,146,169,177,180,188],"past":[27],"few":[28],"years":[29],"several":[30],"probabilistic":[31],"graphical":[32],"models":[33,52,132,173],"have":[34,53,76],"been":[35,77],"proposed":[36],"address":[38],"this":[39,106],"problem,":[40],"most":[41],"of":[42,82,112,123,136,148,171,176,179,187,190],"them":[43,64],"based":[44],"on":[45,156],"Latent":[46],"Dirichlet":[47],"Allocation":[48],"(LDA).":[49],"While":[50,86],"these":[51,131],"a":[54,92,110,121,157],"lot":[55],"in":[56,79,174,185],"common,":[57],"there":[58,99],"are":[59],"some":[60],"characteristics":[61],"that":[62,75,91,130],"distinguish":[63,145],"each":[66],"other.":[67],"These":[68],"fundamental":[69],"differences":[70],"correspond":[71],"major":[73,138],"decisions":[74],"made":[78],"design":[81,113,150],"LDA":[84,126],"models.":[85,127],"research":[87],"papers":[88],"typically":[89],"claim":[90],"new":[93],"model":[94],"outperforms":[95],"existing":[97],"ones,":[98],"is":[100],"normally":[101],"no":[102],"\"one-size-fits-all\"":[103],"model.":[104],"paper,":[107],"we":[108],"present":[109],"set":[111,183],"guidelines":[114],"aspect-based":[116],"mining":[118],"by":[119],"discussing":[120],"series":[122],"increasingly":[124],"sophisticated":[125],"We":[128,152],"argue":[129],"represent":[133],"essence":[135],"published":[139],"methods":[140],"allow":[142],"us":[143],"impact":[147],"various":[149],"conduct":[153],"extensive":[154],"experiments":[155],"large":[159],"real":[160],"life":[161],"dataset":[162],"Epinions.com":[164],"(500K":[165],"reviews)":[166],"compare":[168],"performance":[170],"different":[172],"terms":[175,186],"likelihood":[178],"held-out":[181],"test":[182],"accuracy":[189],"aspect":[191],"identification":[192],"rating":[194],"prediction.":[195]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":23},{"year":2017,"cited_by_count":16},{"year":2016,"cited_by_count":20},{"year":2015,"cited_by_count":23},{"year":2014,"cited_by_count":16},{"year":2013,"cited_by_count":7}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
