{"id":"https://openalex.org/W3091902754","doi":"https://doi.org/10.1145/3399630","title":"A Weakly Supervised WordNet-Guided Deep Learning Approach to Extracting Aspect Terms from Online Reviews","display_name":"A Weakly Supervised WordNet-Guided Deep Learning Approach to Extracting Aspect Terms from Online Reviews","publication_year":2020,"publication_date":"2020-07-21","ids":{"openalex":"https://openalex.org/W3091902754","doi":"https://doi.org/10.1145/3399630","mag":"3091902754"},"language":"en","primary_location":{"id":"doi:10.1145/3399630","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3399630","pdf_url":null,"source":{"id":"https://openalex.org/S4210170305","display_name":"ACM Transactions on Management Information Systems","issn_l":"2158-656X","issn":["2158-656X","2158-6578"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Management Information Systems","raw_type":"journal-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/A5000963620","display_name":"Jie Tao","orcid":"https://orcid.org/0000-0002-8958-561X"},"institutions":[{"id":"https://openalex.org/I126350171","display_name":"Fairfield University","ror":"https://ror.org/04z49n283","country_code":"US","type":"education","lineage":["https://openalex.org/I126350171"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jie Tao","raw_affiliation_strings":["Fairfield University, Fairfield, Connecticut"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fairfield University, Fairfield, Connecticut","institution_ids":["https://openalex.org/I126350171"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010146682","display_name":"Lina Zhou","orcid":"https://orcid.org/0000-0003-1864-0527"},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lina Zhou","raw_affiliation_strings":["University of North Carolina at Charlotte, Charlotte, North Carolina"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Carolina at Charlotte, Charlotte, North Carolina","institution_ids":["https://openalex.org/I102149020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5000963620"],"corresponding_institution_ids":["https://openalex.org/I126350171"],"apc_list":null,"apc_paid":null,"fwci":0.8161,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.79522323,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"11","issue":"3","first_page":"1","last_page":"22"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","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/T13083","display_name":"Advanced Text Analysis Techniques","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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9988999962806702,"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":0.9975000023841858,"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/wordnet","display_name":"WordNet","score":0.8872822523117065},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8050453662872314},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6605588793754578},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6422035098075867},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.532981812953949},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5037493109703064},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.48285800218582153},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.47650232911109924},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4596502482891083},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4557887613773346},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43972668051719666},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.43018895387649536},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.38310837745666504}],"concepts":[{"id":"https://openalex.org/C157659113","wikidata":"https://www.wikidata.org/wiki/Q533822","display_name":"WordNet","level":2,"score":0.8872822523117065},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8050453662872314},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6605588793754578},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6422035098075867},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.532981812953949},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5037493109703064},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.48285800218582153},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.47650232911109924},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4596502482891083},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4557887613773346},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43972668051719666},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.43018895387649536},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.38310837745666504},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3399630","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3399630","pdf_url":null,"source":{"id":"https://openalex.org/S4210170305","display_name":"ACM Transactions on Management Information Systems","issn_l":"2158-656X","issn":["2158-656X","2158-6578"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Management Information Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1603598191","https://openalex.org/W1970264778","https://openalex.org/W2009118069","https://openalex.org/W2028538363","https://openalex.org/W2029080598","https://openalex.org/W2062331696","https://openalex.org/W2077259488","https://openalex.org/W2081580037","https://openalex.org/W2094244309","https://openalex.org/W2127709903","https://openalex.org/W2145827727","https://openalex.org/W2290466703","https://openalex.org/W2399358428","https://openalex.org/W2427312199","https://openalex.org/W2436001372","https://openalex.org/W2511832088","https://openalex.org/W2534712034","https://openalex.org/W2546935677","https://openalex.org/W2560674852","https://openalex.org/W2588293823","https://openalex.org/W2592270044","https://openalex.org/W2598013184","https://openalex.org/W2600278912","https://openalex.org/W2606092111","https://openalex.org/W2612360215","https://openalex.org/W2614093935","https://openalex.org/W2622365670","https://openalex.org/W2626561952","https://openalex.org/W2726474813","https://openalex.org/W2745475103","https://openalex.org/W2757016771","https://openalex.org/W2762466482","https://openalex.org/W2768558358","https://openalex.org/W2781474777","https://openalex.org/W2784280741","https://openalex.org/W2789364533","https://openalex.org/W2791707061","https://openalex.org/W2801566476","https://openalex.org/W2888032469","https://openalex.org/W2888501547","https://openalex.org/W2895547478","https://openalex.org/W2899626049","https://openalex.org/W2930957955","https://openalex.org/W2950577311","https://openalex.org/W2963491860","https://openalex.org/W2963874170","https://openalex.org/W3011759460","https://openalex.org/W3123967386","https://openalex.org/W3125952890","https://openalex.org/W4403159200"],"related_works":["https://openalex.org/W1499795620","https://openalex.org/W2898959159","https://openalex.org/W112738600","https://openalex.org/W252296781","https://openalex.org/W2135707795","https://openalex.org/W1537146566","https://openalex.org/W1517978361","https://openalex.org/W1540103129","https://openalex.org/W2069398544","https://openalex.org/W2040535673"],"abstract_inverted_index":{"The":[0,24,136,162,190,226],"unstructured":[1],"nature":[2],"of":[3,20,26,67,88,111,158,172,180,239],"online":[4,240],"reviews":[5,196],"makes":[6],"it":[7],"inefficient":[8],"and":[9,16,35,125,187,210,223,233],"inconvenient":[10],"for":[11,33,216,236],"prospective":[12],"consumers":[13],"to":[14,94,132,145],"research":[15,227],"use":[17],"in":[18,52,62,71,96,114],"support":[19],"purchase":[21],"decision":[22],"making.":[23],"aspects":[25],"products":[27],"provide":[28],"a":[29,64,168,177],"fine-grained":[30],"meaningful":[31],"perspective":[32],"understanding":[34],"organizing":[36],"review":[37],"texts.":[38],"Traditional":[39],"aspect":[40,72,133,159,217],"term":[41,73,134,218],"extraction":[42,74,219],"approaches":[43],"rely":[44],"on":[45,139],"discrete":[46,208],"language":[47,57,82,214],"models":[48,58,83,209,215],"that":[49,55,198],"treat":[50],"words":[51],"isolation.":[53],"Despite":[54],"continuous-space":[56,81,213],"have":[59,229],"demonstrated":[60],"promise":[61],"addressing":[63],"wide":[65],"range":[66],"problems,":[68],"their":[69],"application":[70],"faces":[75],"significant":[76],"challenges.":[77],"For":[78],"instance,":[79],"existing":[80],"typically":[84],"require":[85],"large":[86],"collections":[87],"labeled":[89,173],"data,":[90],"which":[91],"remain":[92],"difficult":[93],"obtain":[95],"many":[97],"domains.":[98],"More":[99],"importantly,":[100],"previous":[101],"methods":[102,206],"are":[103],"largely":[104],"data":[105],"driven":[106],"but":[107,154],"overlook":[108],"the":[109,149,156,181,211],"role":[110],"human":[112],"knowledge":[113],"guiding":[115],"model":[116],"development.":[117],"To":[118],"address":[119],"these":[120],"limitations,":[121],"this":[122],"study":[123],"designs":[124],"develops":[126],"weakly":[127],"supervised":[128],"WordNet-guided":[129],"deep":[130],"learning":[131],"extraction.":[135],"approach":[137],"draws":[138],"deep-level":[140],"semantic":[141],"information":[142],"from":[143],"WordNet":[144],"guide":[146],"not":[147],"only":[148],"selection":[150],"representative":[151],"seed":[152],"terms":[153],"also":[155],"pruning":[157],"candidate":[160],"terms.":[161],"weak":[163],"supervision":[164],"is":[165],"provided":[166],"by":[167],"very":[169],"small":[170],"set":[171],"data.":[174],"We":[175],"conduct":[176],"comprehensive":[178],"evaluation":[179,191],"proposed":[182,200],"method":[183,201],"using":[184],"both":[185,221],"direct":[186,222],"indirect":[188,224],"methods.":[189],"results":[192],"with":[193],"Yelp":[194],"restaurant":[195],"demonstrate":[197],"our":[199],"consistently":[202],"outperforms":[203],"all":[204],"baseline":[205],"including":[207],"state-of-the-art":[212],"across":[220],"evaluations.":[225],"findings":[228],"broad":[230],"research,":[231],"technical,":[232],"practical":[234],"implications":[235],"various":[237],"stakeholders":[238],"reviews.":[241]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":5}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
