{"id":"https://openalex.org/W4384284067","doi":"https://doi.org/10.1109/tcss.2023.3290558","title":"An NLP-Deep Learning Approach for Product Rating Prediction Based on Online Reviews and Product Features","display_name":"An NLP-Deep Learning Approach for Product Rating Prediction Based on Online Reviews and Product Features","publication_year":2023,"publication_date":"2023-07-14","ids":{"openalex":"https://openalex.org/W4384284067","doi":"https://doi.org/10.1109/tcss.2023.3290558"},"language":"en","primary_location":{"id":"doi:10.1109/tcss.2023.3290558","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcss.2023.3290558","pdf_url":null,"source":{"id":"https://openalex.org/S2490693980","display_name":"IEEE Transactions on Computational Social Systems","issn_l":"2329-924X","issn":["2329-924X","2373-7476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computational Social 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/A5092468903","display_name":"Tolou Amirifar","orcid":"https://orcid.org/0009-0008-7936-454X"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Tolou Amirifar","raw_affiliation_strings":["Department of Mechanical, Industrial and Aerospace Engineering, Gina School of Engineering, Concordia University, Montreal, QC, Canada","Department of Mechanical, Industrial and Aerospace Engineering, Gina School of Engineering, Concordia University, Montreal, Canada"],"raw_orcid":"https://orcid.org/0009-0008-7936-454X","affiliations":[{"raw_affiliation_string":"Department of Mechanical, Industrial and Aerospace Engineering, Gina School of Engineering, Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Mechanical, Industrial and Aerospace Engineering, Gina School of Engineering, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049507437","display_name":"Salim Lahmiri","orcid":"https://orcid.org/0000-0002-9237-4100"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Salim Lahmiri","raw_affiliation_strings":["Department of Supply Chain and Business Technology Management, John Molson School of Business, Concordia University, Montreal, QC, Canada","Department of Supply Chain and Business Technology Management, John Molson School of Business, Concordia University, Montreal, Canada"],"raw_orcid":"https://orcid.org/0000-0002-9237-4100","affiliations":[{"raw_affiliation_string":"Department of Supply Chain and Business Technology Management, John Molson School of Business, Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Supply Chain and Business Technology Management, John Molson School of Business, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091590459","display_name":"Masoumeh Kazemi Zanjani","orcid":"https://orcid.org/0000-0002-5649-3129"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Masoumeh Kazemi Zanjani","raw_affiliation_strings":["Department of Mechanical, Industrial and Aerospace Engineering, Gina School of Engineering, Concordia University, Montreal, QC, Canada","Department of Mechanical, Industrial and Aerospace Engineering, Gina School of Engineering, Concordia University, Montreal, Canada"],"raw_orcid":"https://orcid.org/0000-0002-5649-3129","affiliations":[{"raw_affiliation_string":"Department of Mechanical, Industrial and Aerospace Engineering, Gina School of Engineering, Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Mechanical, Industrial and Aerospace Engineering, Gina School of Engineering, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":2.4328,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.90835852,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"11","issue":"6","first_page":"8156","last_page":"8168"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9976000189781189,"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":0.9976000189781189,"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/T10609","display_name":"Digital Marketing and Social Media","score":0.9617000222206116,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9140999913215637,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7255595922470093},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6905443668365479},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6403321027755737},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6236627101898193},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.597583532333374},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.5298492908477783},{"id":"https://openalex.org/keywords/strengths-and-weaknesses","display_name":"Strengths and weaknesses","score":0.52193284034729},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.5143090486526489},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5031797289848328},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.4338371753692627},{"id":"https://openalex.org/keywords/laptop","display_name":"Laptop","score":0.422129362821579},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4021286368370056},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14435377717018127}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7255595922470093},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6905443668365479},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6403321027755737},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6236627101898193},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.597583532333374},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.5298492908477783},{"id":"https://openalex.org/C63882131","wikidata":"https://www.wikidata.org/wiki/Q17122954","display_name":"Strengths and weaknesses","level":2,"score":0.52193284034729},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.5143090486526489},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5031797289848328},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.4338371753692627},{"id":"https://openalex.org/C2780008327","wikidata":"https://www.wikidata.org/wiki/Q3962","display_name":"Laptop","level":2,"score":0.422129362821579},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4021286368370056},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14435377717018127},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tcss.2023.3290558","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcss.2023.3290558","pdf_url":null,"source":{"id":"https://openalex.org/S2490693980","display_name":"IEEE Transactions on Computational Social Systems","issn_l":"2329-924X","issn":["2329-924X","2373-7476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computational Social Systems","raw_type":"journal-article"},{"id":"pmh:oai:https://spectrum.library.concordia.ca:990578","is_oa":false,"landing_page_url":"https://spectrum.library.concordia.ca/id/eprint/990578/2/Amirifar_MASc_F2022.pdf","pdf_url":"https://spectrum.library.concordia.ca/id/eprint/990578/2/Amirifar_MASc_F2022.pdf","source":{"id":"https://openalex.org/S4306400871","display_name":"Spectrum Research Repository (Concordia University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I60158472","host_organization_name":"Concordia University","host_organization_lineage":["https://openalex.org/I60158472"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Thesis"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.49000000953674316,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W173870552","https://openalex.org/W1528741131","https://openalex.org/W1964168965","https://openalex.org/W2019759670","https://openalex.org/W2022810935","https://openalex.org/W2062848325","https://openalex.org/W2067878879","https://openalex.org/W2070769602","https://openalex.org/W2098700435","https://openalex.org/W2131774270","https://openalex.org/W2136848157","https://openalex.org/W2143345705","https://openalex.org/W2144578941","https://openalex.org/W2152907901","https://openalex.org/W2159457224","https://openalex.org/W2334378031","https://openalex.org/W2491635235","https://openalex.org/W2770835535","https://openalex.org/W2803875933","https://openalex.org/W2887651422","https://openalex.org/W2896163943","https://openalex.org/W2904063213","https://openalex.org/W2921330598","https://openalex.org/W2936143922","https://openalex.org/W2941195982","https://openalex.org/W2945943453","https://openalex.org/W2963273475","https://openalex.org/W2972260529","https://openalex.org/W2975867066","https://openalex.org/W2980000166","https://openalex.org/W2980857480","https://openalex.org/W2983883274","https://openalex.org/W2991061473","https://openalex.org/W2997690421","https://openalex.org/W3004596289","https://openalex.org/W3009102234","https://openalex.org/W3011375118","https://openalex.org/W3014675721","https://openalex.org/W3026689951","https://openalex.org/W3026908503","https://openalex.org/W3035784470","https://openalex.org/W3043457491","https://openalex.org/W3045625912","https://openalex.org/W3099190990","https://openalex.org/W4206050165","https://openalex.org/W4206797693","https://openalex.org/W6600115116","https://openalex.org/W6634357899","https://openalex.org/W6636510571","https://openalex.org/W6682839988","https://openalex.org/W6752909555","https://openalex.org/W6755207826","https://openalex.org/W6787443430","https://openalex.org/W6829846596"],"related_works":["https://openalex.org/W2368605798","https://openalex.org/W2518037665","https://openalex.org/W2045444909","https://openalex.org/W2348524959","https://openalex.org/W2477036161","https://openalex.org/W3153292769","https://openalex.org/W2368049389","https://openalex.org/W2170801710","https://openalex.org/W2384861574","https://openalex.org/W2952704802"],"abstract_inverted_index":{"This":[0],"study":[1],"focuses":[2],"on":[3,11,21,27,83,162],"predicting":[4,22],"the":[5,23,31,34,39,67,72,78,118,122,129,137,140,146,152,157,173,186,189,194,199,223,236],"popularity":[6,47],"of":[7,33,41,59,139,156,165,172,188,203,226,230,238],"a":[8,56,163],"product":[9,42,74,80,183],"based":[10,26],"its":[12,46],"overall":[13,68,153],"rating":[14,25,69,154],"score.":[15,215],"Unlike":[16],"previous":[17],"studies":[18],"that":[19,114],"focus":[20],"review":[24],"sentiment":[28],"analysis":[29],"and":[30,51,102,142,211,228],"polarity":[32],"reviews,":[35],"in":[36,44,185,245],"this":[37,54,217],"research,":[38],"effect":[40],"features":[43,113,144,184],"determining":[45],"is":[48,62,177],"directly":[49],"measured":[50],"analyzed.":[52],"To":[53],"end,":[55],"methodology":[57],"consisting":[58],"three":[60,87],"phases":[61],"considered.":[63],"Phase":[64,109,134],"1":[65],"predicts":[66],"by":[70,120],"feeding":[71],"general":[73,141],"features,":[75],"extracted":[76],"from":[77,235],"online":[79,131],"information":[81],"available":[82],"Amazon":[84],"webpages":[85],"to":[86,128,145,150,180,243],"different":[88],"deep":[89,93],"learning":[90],"(DL)":[91],"models:":[92],"feedforward":[94],"neural":[95,99,106],"network":[96,100,107],"(DFFNN),":[97],"probabilistic":[98],"(PNN),":[101],"radial":[103],"basis":[104],"function":[105],"(RBFNN).":[108],"2":[110],"identifies":[111],"other":[112],"customers":[115],"care":[116],"about":[117],"most":[119],"applying":[121],"named":[123],"entity":[124],"recognition":[125],"(NER)":[126],"algorithm":[127],"customer":[130],"reviews.":[132],"Finally,":[133],"3":[135],"feeds":[136],"combination":[138],"custom":[143,182],"same":[147],"DL":[148,190],"models":[149],"predict":[151],"score":[155,202],"product.":[158],"The":[159],"experimental":[160],"results":[161],"dataset":[164],"laptop":[166],"products":[167,232],"indicate":[168],"an":[169],"impressive":[170],"performance":[171],"proposed":[174,195],"approach,":[175],"which":[176],"mainly":[178],"attributed":[179],"including":[181],"inputs":[187],"algorithm.":[191],"More":[192],"precisely,":[193],"model":[196],"could":[197,219],"achieve":[198],"highest":[200],"accuracy":[201],"84.01%,":[204],"84.68%":[205],"for":[206,209,213],"recall,":[207],"87.63%":[208],"precision,":[210],"84.06%":[212],"F1":[214],"Applying":[216],"procedure":[218],"help":[220],"businesses":[221],"identify":[222],"specific":[224],"areas":[225],"strengths":[227],"weaknesses":[229],"their":[231,239],"or":[233],"services":[234],"perspective":[237],"customers,":[240],"allowing":[241],"them":[242],"thrive":[244],"today\u2019s":[246],"competitive":[247],"markets.":[248]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":9}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}
