{"id":"https://openalex.org/W4416765081","doi":"https://doi.org/10.3390/e27121202","title":"Transformer and Pre-Transformer Model-Based Sentiment Prediction with Various Embeddings: A Case Study on Amazon Reviews","display_name":"Transformer and Pre-Transformer Model-Based Sentiment Prediction with Various Embeddings: A Case Study on Amazon Reviews","publication_year":2025,"publication_date":"2025-11-27","ids":{"openalex":"https://openalex.org/W4416765081","doi":"https://doi.org/10.3390/e27121202","pmid":"https://pubmed.ncbi.nlm.nih.gov/41440405"},"language":"en","primary_location":{"id":"doi:10.3390/e27121202","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e27121202","pdf_url":null,"source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/e27121202","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051120332","display_name":"\u0130smail Duru","orcid":"https://orcid.org/0000-0002-4005-4818"},"institutions":[{"id":"https://openalex.org/I4210092500","display_name":"T\u00fcrk Telekom (Turkey)","ror":"https://ror.org/002famp79","country_code":"TR","type":"company","lineage":["https://openalex.org/I4210092500"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ismail Duru","raw_affiliation_strings":["R&D Department, T\u00fcrk Telekom, Ankara 06103, Turkey"],"raw_orcid":"https://orcid.org/0000-0002-4005-4818","affiliations":[{"raw_affiliation_string":"R&D Department, T\u00fcrk Telekom, Ankara 06103, Turkey","institution_ids":["https://openalex.org/I4210092500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088925999","display_name":"Ay\u015fe Saliha Sunar","orcid":"https://orcid.org/0000-0002-0836-5616"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]},{"id":"https://openalex.org/I41055640","display_name":"Bitlis Eren University","ror":"https://ror.org/00mm4ys28","country_code":"TR","type":"education","lineage":["https://openalex.org/I41055640"]}],"countries":["GB","TR"],"is_corresponding":true,"raw_author_name":"Ay\u015fe Saliha Sunar","raw_affiliation_strings":["Department of Computer Engineering, Bitlis Eren University, Bitlis 13000, Turkey","Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK"],"raw_orcid":"https://orcid.org/0000-0002-0836-5616","affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Bitlis Eren University, Bitlis 13000, Turkey","institution_ids":["https://openalex.org/I41055640"]},{"raw_affiliation_string":"Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK","institution_ids":["https://openalex.org/I39555362"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5088925999"],"corresponding_institution_ids":["https://openalex.org/I39555362","https://openalex.org/I41055640"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18869842,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"27","issue":"12","first_page":"1202","last_page":"1202"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9135000109672546,"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":0.9135000109672546,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.019300000742077827,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.00989999994635582,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.8270999789237976},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6718000173568726},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.49970000982284546},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.49959999322891235},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.45410001277923584},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.40630000829696655},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3521000146865845},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.3463999927043915}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.8270999789237976},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7562000155448914},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6718000173568726},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5464000105857849},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5324000120162964},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.49970000982284546},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.49959999322891235},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45969998836517334},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.45410001277923584},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.40630000829696655},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3521000146865845},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3463999927043915},{"id":"https://openalex.org/C535291247","wikidata":"https://www.wikidata.org/wiki/Q177567","display_name":"Amazon rainforest","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.323199987411499},{"id":"https://openalex.org/C96405632","wikidata":"https://www.wikidata.org/wiki/Q1128416","display_name":"Consumer confidence index","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.29030001163482666},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2567000091075897}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/e27121202","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e27121202","pdf_url":null,"source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:41440405","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41440405","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:3b15e14b486b44458846447ef1e8eb79","is_oa":true,"landing_page_url":"https://doaj.org/article/3b15e14b486b44458846447ef1e8eb79","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 27, Iss 12, p 1202 (2025)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:12731383","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12731383/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e27121202","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e27121202","pdf_url":null,"source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1980867644","https://openalex.org/W2064675550","https://openalex.org/W2101746535","https://openalex.org/W2129034170","https://openalex.org/W2160660844","https://openalex.org/W2250539671","https://openalex.org/W2306706380","https://openalex.org/W2327501763","https://openalex.org/W2896337548","https://openalex.org/W2923841521","https://openalex.org/W2945808722","https://openalex.org/W2947838898","https://openalex.org/W2963929190","https://openalex.org/W2964236337","https://openalex.org/W2964288660","https://openalex.org/W2971088377","https://openalex.org/W2982654872","https://openalex.org/W3008856892","https://openalex.org/W3022228835","https://openalex.org/W3031696893","https://openalex.org/W3033916895","https://openalex.org/W3039554467","https://openalex.org/W3102970018","https://openalex.org/W3146366485","https://openalex.org/W3162985935","https://openalex.org/W3165519921","https://openalex.org/W3178048855","https://openalex.org/W3213984000","https://openalex.org/W4205184193","https://openalex.org/W4206706211","https://openalex.org/W4212946661","https://openalex.org/W4223591627","https://openalex.org/W4280599861","https://openalex.org/W4281490773","https://openalex.org/W4284888897","https://openalex.org/W4289861361","https://openalex.org/W4296873064","https://openalex.org/W4311963445","https://openalex.org/W4321351836","https://openalex.org/W4362475844","https://openalex.org/W4393357787","https://openalex.org/W4403276220","https://openalex.org/W4403722863","https://openalex.org/W4406857309","https://openalex.org/W4407365075","https://openalex.org/W4409882512","https://openalex.org/W4413228182"],"related_works":[],"abstract_inverted_index":{"Sentiment":[0],"analysis":[1,110,216],"is":[2],"essential":[3],"for":[4,233],"understanding":[5,148],"consumer":[6],"opinions,":[7],"yet":[8],"selecting":[9],"the":[10,52,151,167,177,196],"optimal":[11],"models":[12,38,144],"and":[13,48,70,81,97,131,149,155,217],"embedding":[14,63],"methods":[15],"remains":[16],"challenging,":[17],"especially":[18],"when":[19],"handling":[20],"ambiguous":[21],"expressions,":[22],"slang,":[23],"or":[24],"mismatched":[25],"sentiment-rating":[26],"pairs.":[27],"This":[28,210],"study":[29,211],"provides":[30],"a":[31,60,89,107,191,222],"comprehensive":[32],"comparative":[33],"evaluation":[34,91,225],"of":[35,62,111,118,169],"sentiment":[36,215],"classification":[37],"across":[39],"three":[40],"paradigms:":[41],"traditional":[42,130],"machine":[43],"learning,":[44,47],"pre-transformer":[45],"deep":[46],"transformer-based":[49],"models.":[50],"Using":[51],"Amazon":[53,178],"Magazine":[54],"Subscriptions":[55],"2023":[56],"dataset,":[57,182],"we":[58,84,105,172],"evaluate":[59],"range":[61],"techniques,":[64],"including":[65],"static":[66],"embeddings":[67,73],"(GloVe,":[68],"FastText)":[69],"contextual":[71,147],"transformer":[72],"(BERT,":[74],"DistilBERT,":[75,161],"etc.).":[76],"To":[77,165],"capture":[78],"predictive":[79],"confidence":[80],"model":[82,231],"uncertainty,":[83],"include":[85],"categorical":[86],"cross-entropy":[87,157],"as":[88],"key":[90],"metric":[92],"alongside":[93],"accuracy,":[94],"precision,":[95],"recall,":[96,136],"F1-score.":[98],"In":[99],"addition":[100],"to":[101,114,138,202,205,213],"detailed":[102],"quantitative":[103],"comparisons,":[104],"conduct":[106],"systematic":[108],"qualitative":[109],"misclassified":[112],"samples":[113],"reveal":[115],"model-specific":[116],"patterns":[117],"uncertainty.":[119],"Our":[120],"findings":[121],"show":[122],"that":[123,227],"FastText":[124],"consistently":[125],"outperforms":[126],"GloVe":[127],"in":[128,135],"both":[129,214],"LSTM-based":[132],"models,":[133],"particularly":[134],"due":[137],"its":[139],"subword-level":[140],"semantic":[141],"richness.":[142],"Transformer-based":[143],"demonstrate":[145],"superior":[146],"achieve":[150],"highest":[152],"accuracy":[153],"(92%)":[154],"lowest":[156],"loss":[158],"(0.25)":[159],"with":[160],"indicating":[162],"well-calibrated":[163],"predictions.":[164],"validate":[166],"generalisability":[168],"our":[170,174],"results,":[171],"replicated":[173],"experiments":[175],"on":[176],"Gift":[179],"Card":[180],"Reviews":[181],"where":[183],"similar":[184],"trends":[185],"were":[186],"observed.":[187],"We":[188],"also":[189],"adopt":[190],"resource-aware":[192],"approach":[193],"by":[194,220],"reducing":[195],"dataset":[197],"size":[198],"from":[199],"25":[200],"K":[201,204],"20":[203],"reflect":[206],"real-world":[207],"hardware":[208],"constraints.":[209],"contributes":[212],"sustainable":[218],"AI":[219],"offering":[221],"scalable,":[223],"entropy-aware":[224],"framework":[226],"supports":[228],"informed,":[229],"context-sensitive":[230],"selection":[232],"practical":[234],"applications.":[235]},"counts_by_year":[],"updated_date":"2025-12-25T23:15:44.422516","created_date":"2025-11-28T00:00:00"}
