{"id":"https://openalex.org/W1983286042","doi":"https://doi.org/10.1145/1277741.1277845","title":"ARSA","display_name":"ARSA","publication_year":2007,"publication_date":"2007-07-23","ids":{"openalex":"https://openalex.org/W1983286042","doi":"https://doi.org/10.1145/1277741.1277845","mag":"1983286042"},"language":"en","primary_location":{"id":"doi:10.1145/1277741.1277845","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1277741.1277845","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th annual 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/A5100355768","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0002-0166-3944"},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["York University, Toronto, ON, Canada","York University , Toronto , ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"York University, Toronto, ON, Canada","institution_ids":["https://openalex.org/I192455969"]},{"raw_affiliation_string":"York University , Toronto , ON, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiangji Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xiangji Huang","raw_affiliation_strings":["York University, Toronto, ON, Canada","York University , Toronto , ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"York University, Toronto, ON, Canada","institution_ids":["https://openalex.org/I192455969"]},{"raw_affiliation_string":"York University , Toronto , ON, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009394818","display_name":"Aijun An","orcid":"https://orcid.org/0000-0003-1765-5751"},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Aijun An","raw_affiliation_strings":["York University, Toronto, ON, Canada","York University , Toronto , ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"York University, Toronto, ON, Canada","institution_ids":["https://openalex.org/I192455969"]},{"raw_affiliation_string":"York University , Toronto , ON, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071285426","display_name":"Xiaohui Yu","orcid":"https://orcid.org/0000-0001-8170-2327"},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xiaohui Yu","raw_affiliation_strings":["York University, Toronto, ON, Canada","York University , Toronto , ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"York University, Toronto, ON, Canada","institution_ids":["https://openalex.org/I192455969"]},{"raw_affiliation_string":"York University , Toronto , ON, Canada","institution_ids":["https://openalex.org/I192455969"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I192455969"],"apc_list":null,"apc_paid":null,"fwci":6.9084,"has_fulltext":false,"cited_by_count":310,"citation_normalized_percentile":{"value":0.97873931,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"607","last_page":"614"},"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/T11644","display_name":"Spam and Phishing Detection","score":0.9986000061035156,"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"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9983000159263611,"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/probabilistic-latent-semantic-analysis","display_name":"Probabilistic latent semantic analysis","score":0.8444552421569824},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7919554710388184},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.7241355180740356},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.7139988541603088},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.5505830645561218},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5122467875480652},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.48160645365715027},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.45610007643699646},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.42568355798721313},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3790968656539917},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3422659635543823},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.06900084018707275}],"concepts":[{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.8444552421569824},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7919554710388184},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.7241355180740356},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7139988541603088},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.5505830645561218},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5122467875480652},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.48160645365715027},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.45610007643699646},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.42568355798721313},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3790968656539917},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3422659635543823},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.06900084018707275},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1277741.1277845","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1277741.1277845","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1593338527","https://openalex.org/W1612003148","https://openalex.org/W1880262756","https://openalex.org/W1982381099","https://openalex.org/W2021093430","https://openalex.org/W2021314079","https://openalex.org/W2049633694","https://openalex.org/W2064906988","https://openalex.org/W2066727938","https://openalex.org/W2081471474","https://openalex.org/W2085582472","https://openalex.org/W2095426726","https://openalex.org/W2107503076","https://openalex.org/W2107610218","https://openalex.org/W2108122190","https://openalex.org/W2113889316","https://openalex.org/W2114524997","https://openalex.org/W2141631351","https://openalex.org/W2150739536","https://openalex.org/W2155328222","https://openalex.org/W2163425655","https://openalex.org/W2163455955","https://openalex.org/W2166706824","https://openalex.org/W2166900514","https://openalex.org/W2167439930","https://openalex.org/W3146306708","https://openalex.org/W6635469432","https://openalex.org/W6639619044"],"related_works":["https://openalex.org/W2921491680","https://openalex.org/W2082325506","https://openalex.org/W2784194212","https://openalex.org/W2982493961","https://openalex.org/W2251863249","https://openalex.org/W2132052677","https://openalex.org/W4291700620","https://openalex.org/W4391903805","https://openalex.org/W2087743880","https://openalex.org/W4389937858"],"abstract_inverted_index":{"Due":[0],"to":[1,44,96,117],"its":[2],"high":[3],"popularity,":[4],"Weblogs":[5],"(or":[6],"blogs":[7,40],"in":[8,20,68,106],"short)":[9],"present":[10,111],"a":[11,70,76,80,98,135,157,160],"wealth":[12],"of":[13,35,57,61,82,101,171],"information":[14,38,47,104,121],"that":[15,145],"can":[16],"be":[17],"very":[18],"helpful":[19],"assessing":[21],"the":[22,33,58,91,102,107,119,151,167,172],"general":[23],"public's":[24],"sentiments":[25],"and":[26,41,169],"opinions.":[27],"In":[28],"this":[29],"paper,":[30],"we":[31,63],"study":[32],"problem":[34],"mining":[36],"sentiment":[37,84,103,120,152],"from":[39],"investigate":[42],"ways":[43],"use":[45],"such":[46],"for":[48,125],"predicting":[49,126],"product":[50,127],"sales":[51,128],"performance.":[52,129],"Based":[53],"on":[54,90,134],"an":[55,87,113],"analysis":[56],"complex":[59],"nature":[60],"sentiments,":[62],"propose":[64],"Sentiment":[65],"PLSA":[66],"(S-PLSA),":[67],"which":[69],"blog":[71,92],"entry":[72],"is":[73],"viewed":[74],"as":[75,154,156],"document":[77],"generated":[78],"by":[79,123],"number":[81],"hidden":[83],"factors.":[85],"Training":[86],"S-PLSA":[88,124],"model":[89,158],"data":[93,137],"enables":[94],"us":[95],"obtain":[97],"succinct":[99],"summary":[100],"embedded":[105],"blogs.":[108],"We":[109,139],"then":[110],"ARSA,":[112],"autoregressive":[114],"sentiment-aware":[115],"model,":[116],"utilize":[118],"captured":[122],"Extensive":[130],"experiments":[131],"were":[132],"conducted":[133],"movie":[136],"set.":[138],"compare":[140],"ARSA":[141],"with":[142,159],"alternative":[143],"models":[144],"do":[146],"not":[147],"take":[148],"into":[149],"account":[150],"information,":[153],"well":[155],"different":[161],"feature":[162],"selection":[163],"method.":[164],"Experiments":[165],"confirm":[166],"effectiveness":[168],"superiority":[170],"proposed":[173],"approach.":[174]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":41},{"year":2019,"cited_by_count":27},{"year":2018,"cited_by_count":15},{"year":2017,"cited_by_count":26},{"year":2016,"cited_by_count":15},{"year":2015,"cited_by_count":39},{"year":2014,"cited_by_count":18},{"year":2013,"cited_by_count":21},{"year":2012,"cited_by_count":21}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2016-06-24T00:00:00"}