{"id":"https://openalex.org/W4205313219","doi":"https://doi.org/10.1109/bigdata52589.2021.9671939","title":"A Big Data Experiment to Evaluate the Effectiveness of Traditional Machine Learning Techniques Against LSTM Neural Networks in the Hotels Clients Opinion Mining","display_name":"A Big Data Experiment to Evaluate the Effectiveness of Traditional Machine Learning Techniques Against LSTM Neural Networks in the Hotels Clients Opinion Mining","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205313219","doi":"https://doi.org/10.1109/bigdata52589.2021.9671939"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671939","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671939","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","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/A5069238012","display_name":"Thiago de Oliveira Lima","orcid":null},"institutions":[{"id":"https://openalex.org/I190085865","display_name":"Universidade Federal de Sergipe","ror":"https://ror.org/028ka0n85","country_code":"BR","type":"education","lineage":["https://openalex.org/I190085865"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Thiago de Oliveira Lima","raw_affiliation_strings":["Postgraduate Program in Computer Science - PROCC - Federal University of Sergipe (UFS), S\u00e3o Crist\u00f3v\u00e3o, Sergipe, Brazil"],"affiliations":[{"raw_affiliation_string":"Postgraduate Program in Computer Science - PROCC - Federal University of Sergipe (UFS), S\u00e3o Crist\u00f3v\u00e3o, Sergipe, Brazil","institution_ids":["https://openalex.org/I190085865"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082942140","display_name":"Methanias Cola\u00e7o J\u00fanior","orcid":"https://orcid.org/0000-0002-4811-1477"},"institutions":[{"id":"https://openalex.org/I190085865","display_name":"Universidade Federal de Sergipe","ror":"https://ror.org/028ka0n85","country_code":"BR","type":"education","lineage":["https://openalex.org/I190085865"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Methanias Colaco","raw_affiliation_strings":["Postgraduate Program in Computer Science - PROCC - Federal University of Sergipe (UFS), S\u00e3o Crist\u00f3v\u00e3o, Sergipe, Brazil"],"affiliations":[{"raw_affiliation_string":"Postgraduate Program in Computer Science - PROCC - Federal University of Sergipe (UFS), S\u00e3o Crist\u00f3v\u00e3o, Sergipe, Brazil","institution_ids":["https://openalex.org/I190085865"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040941148","display_name":"Kleber Henrique de Jesus Prado","orcid":"https://orcid.org/0000-0002-3555-9475"},"institutions":[{"id":"https://openalex.org/I190085865","display_name":"Universidade Federal de Sergipe","ror":"https://ror.org/028ka0n85","country_code":"BR","type":"education","lineage":["https://openalex.org/I190085865"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Kleber H. de J. Prado","raw_affiliation_strings":["Postgraduate Program in Computer Science - PROCC - Federal University of Sergipe (UFS), S\u00e3o Crist\u00f3v\u00e3o, Sergipe, Brazil"],"affiliations":[{"raw_affiliation_string":"Postgraduate Program in Computer Science - PROCC - Federal University of Sergipe (UFS), S\u00e3o Crist\u00f3v\u00e3o, Sergipe, Brazil","institution_ids":["https://openalex.org/I190085865"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033120104","display_name":"Israel Dias de J.","orcid":null},"institutions":[{"id":"https://openalex.org/I190085865","display_name":"Universidade Federal de Sergipe","ror":"https://ror.org/028ka0n85","country_code":"BR","type":"education","lineage":["https://openalex.org/I190085865"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Israel Dias de J.","raw_affiliation_strings":["State of Sergipe\u2019s General Accounting Court (TCE-SE), Aracaju, Sergipe, Brazil","State of Sergipe's General Accounting Court (TCE-SE), Aracaju, Sergipe, Brazil"],"affiliations":[{"raw_affiliation_string":"State of Sergipe\u2019s General Accounting Court (TCE-SE), Aracaju, Sergipe, Brazil","institution_ids":["https://openalex.org/I190085865"]},{"raw_affiliation_string":"State of Sergipe's General Accounting Court (TCE-SE), Aracaju, Sergipe, Brazil","institution_ids":["https://openalex.org/I190085865"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019642968","display_name":"Frederico Resende de Oliveira","orcid":null},"institutions":[{"id":"https://openalex.org/I190085865","display_name":"Universidade Federal de Sergipe","ror":"https://ror.org/028ka0n85","country_code":"BR","type":"education","lineage":["https://openalex.org/I190085865"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Frederico Resende de Oliveira","raw_affiliation_strings":["Postgraduate Program in Computer Science - PROCC - Federal University of Sergipe (UFS), S\u00e3o Crist\u00f3v\u00e3o, Sergipe, Brazil"],"affiliations":[{"raw_affiliation_string":"Postgraduate Program in Computer Science - PROCC - Federal University of Sergipe (UFS), S\u00e3o Crist\u00f3v\u00e3o, Sergipe, Brazil","institution_ids":["https://openalex.org/I190085865"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5069238012"],"corresponding_institution_ids":["https://openalex.org/I190085865"],"apc_list":null,"apc_paid":null,"fwci":0.5026,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.66853617,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5199","last_page":"5208"},"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/T10609","display_name":"Digital Marketing and Social Media","score":0.996399998664856,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9943000078201294,"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/computer-science","display_name":"Computer science","score":0.7957965135574341},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7174857258796692},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7067829370498657},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.692272961139679},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6688365340232849},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6359619498252869},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6098101139068604},{"id":"https://openalex.org/keywords/tourism","display_name":"Tourism","score":0.5202828049659729},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48874902725219727},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.45845192670822144},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.42964646220207214},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.42739298939704895},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42263519763946533},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4145057797431946},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.3612179756164551}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7957965135574341},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7174857258796692},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7067829370498657},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.692272961139679},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6688365340232849},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6359619498252869},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6098101139068604},{"id":"https://openalex.org/C18918823","wikidata":"https://www.wikidata.org/wiki/Q49389","display_name":"Tourism","level":2,"score":0.5202828049659729},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48874902725219727},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.45845192670822144},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.42964646220207214},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.42739298939704895},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42263519763946533},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4145057797431946},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.3612179756164551},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671939","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671939","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.6299999952316284}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W183178636","https://openalex.org/W1511267087","https://openalex.org/W1576726980","https://openalex.org/W1614298861","https://openalex.org/W1689711448","https://openalex.org/W1832693441","https://openalex.org/W1988018685","https://openalex.org/W2014902591","https://openalex.org/W2049624350","https://openalex.org/W2049832889","https://openalex.org/W2064675550","https://openalex.org/W2069375627","https://openalex.org/W2101234009","https://openalex.org/W2120400822","https://openalex.org/W2120615054","https://openalex.org/W2171033594","https://openalex.org/W2250539671","https://openalex.org/W2251939518","https://openalex.org/W2296194829","https://openalex.org/W2313008886","https://openalex.org/W2401188729","https://openalex.org/W2472803348","https://openalex.org/W2487770199","https://openalex.org/W2493916176","https://openalex.org/W2521029800","https://openalex.org/W2529550020","https://openalex.org/W2560654344","https://openalex.org/W2562607067","https://openalex.org/W2593762897","https://openalex.org/W2618834153","https://openalex.org/W2728494775","https://openalex.org/W2742947407","https://openalex.org/W2751192449","https://openalex.org/W2756408590","https://openalex.org/W2792503084","https://openalex.org/W2797570337","https://openalex.org/W2810325107","https://openalex.org/W2953384591","https://openalex.org/W2963355447","https://openalex.org/W4211186029","https://openalex.org/W4229977739","https://openalex.org/W4291213652","https://openalex.org/W4298857951","https://openalex.org/W6630481227","https://openalex.org/W6634627538","https://openalex.org/W6636510571","https://openalex.org/W6656661550","https://openalex.org/W6675354045","https://openalex.org/W6691459498","https://openalex.org/W6713134421","https://openalex.org/W6723250868","https://openalex.org/W6726850330","https://openalex.org/W6727807531","https://openalex.org/W6738116255","https://openalex.org/W6742102919","https://openalex.org/W6743304720","https://openalex.org/W6743359967"],"related_works":["https://openalex.org/W2769501189","https://openalex.org/W4315588616","https://openalex.org/W4312773271","https://openalex.org/W2888805565","https://openalex.org/W2962686197","https://openalex.org/W3005513013","https://openalex.org/W2207653751","https://openalex.org/W2611137333","https://openalex.org/W4389543811","https://openalex.org/W4291700620"],"abstract_inverted_index":{"Context:":[0],"Nowadays,":[1],"client":[2],"reviews":[3,98],"on":[4,100],"social":[5,30],"networks":[6,31],"can":[7,32],"be":[8,50],"a":[9],"great":[10],"source":[11],"of":[12,67,93,96,118,123],"knowledge":[13],"extraction":[14],"for":[15,58],"strategic":[16],"marketing":[17],"planning.":[18],"In":[19,37,108],"the":[20,55,63,68,91,101,116,119,139,166,171,182],"tourism":[21,29],"area,":[22],"opinions":[23],"given":[24],"by":[25,121],"hotel":[26,97,103],"clients":[27],"in":[28,35,90],"drive":[33],"improvements":[34],"service.":[36],"this":[38],"context,":[39],"traditional":[40],"text":[41],"mining":[42,95],"techniques":[43],"and":[44,65,82,128,131,177],"new":[45],"deep":[46],"learning":[47],"technologies":[48],"should":[49],"tried":[51],"out":[52],"to":[53,114,165],"select":[54],"best":[56,140],"options":[57],"classifying":[59],"opinions.":[60],"Objective:":[61],"Evaluate":[62],"performance":[64,117,162],"quality":[66,143],"LDA":[69],"(Latent":[70],"Dirichlet":[71],"Allocation),":[72],"Naives":[73],"Bayes":[74],"(NB),":[75],"Logistic":[76],"Regression,":[77],"SVM":[78],"(Support":[79],"Vector":[80],"Machine)":[81],"LSTM":[83,136,157],"-":[84],"Long":[85],"Short-Term":[86],"Memory":[87],"Units":[88],"algorithms":[89],"task":[92],"opinion":[94],"published":[99],"TripAdvisor":[102],"booking":[104],"website.":[105],"Method:":[106],"An":[107],"Vivo":[109],"Controlled":[110],"Experiment":[111],"(Case":[112],"Study)":[113],"compare":[115],"classifiers":[120,184],"means":[122],"accuracy,":[124],"precision,":[125],"recall,":[126],"F1-measure":[127],"average":[129,175],"training":[130,176],"classification":[132,178],"times.":[133],"Results:":[134],"The":[135,156],"model":[137],"presented":[138],"results":[141,151],"regarding":[142,152],"metrics.":[144],"However,":[145],"it":[146],"did":[147],"not":[148],"present":[149],"satisfactory":[150],"processing":[153],"time.":[154],"Conclusion:":[155],"classifier":[158],"had":[159],"clearly":[160],"superior":[161],"when":[163],"compared":[164],"other":[167,172],"evaluated":[168],"ones.":[169],"On":[170],"hand,":[173],"its":[174],"times":[179],"greater":[180],"than":[181],"others":[183],"considered.":[185]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-02-07T06:11:34.122080","created_date":"2025-10-10T00:00:00"}