{"id":"https://openalex.org/W4393035796","doi":"https://doi.org/10.1109/dese60595.2023.10468715","title":"Analyzing Sentiment for Opinion Mining of Large Movie Reviews Using Naive Bayes with Word Frequency","display_name":"Analyzing Sentiment for Opinion Mining of Large Movie Reviews Using Naive Bayes with Word Frequency","publication_year":2023,"publication_date":"2023-12-18","ids":{"openalex":"https://openalex.org/W4393035796","doi":"https://doi.org/10.1109/dese60595.2023.10468715"},"language":"en","primary_location":{"id":"doi:10.1109/dese60595.2023.10468715","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dese60595.2023.10468715","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 16th International Conference on Developments in eSystems Engineering (DeSE)","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/A5029249909","display_name":"Mustafa Abdalrassual Jassim","orcid":"https://orcid.org/0000-0003-3360-6722"},"institutions":[{"id":"https://openalex.org/I501048437","display_name":"Al-Muthanna University","ror":"https://ror.org/03877wr45","country_code":"IQ","type":"education","lineage":["https://openalex.org/I501048437"]}],"countries":["IQ"],"is_corresponding":false,"raw_author_name":"Mustafa Abdalrassual Jassim","raw_affiliation_strings":["Al-Muthanna University, University of Monastir,College of Engineering,Samawah,Iraq","College of Engineering, Al-Muthanna University, University of Monastir, Samawah, Iraq"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Al-Muthanna University, University of Monastir,College of Engineering,Samawah,Iraq","institution_ids":["https://openalex.org/I501048437"]},{"raw_affiliation_string":"College of Engineering, Al-Muthanna University, University of Monastir, Samawah, Iraq","institution_ids":["https://openalex.org/I501048437"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044689198","display_name":"Dhafar Hamed","orcid":"https://orcid.org/0000-0003-0548-0616"},"institutions":[{"id":"https://openalex.org/I27768575","display_name":"University of Anbar","ror":"https://ror.org/055a6gk50","country_code":"IQ","type":"education","lineage":["https://openalex.org/I27768575"]}],"countries":["IQ"],"is_corresponding":false,"raw_author_name":"Dhafar Hamed Abd","raw_affiliation_strings":["University of Anbar,College of Computer Science and Information Technology,Ramadi,Iraq","College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Anbar,College of Computer Science and Information Technology,Ramadi,Iraq","institution_ids":["https://openalex.org/I27768575"]},{"raw_affiliation_string":"College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq","institution_ids":["https://openalex.org/I27768575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049091245","display_name":"Mohamed Nazih Omri","orcid":"https://orcid.org/0000-0001-7803-0179"},"institutions":[{"id":"https://openalex.org/I8636806","display_name":"University of Sousse","ror":"https://ror.org/00dmpgj58","country_code":"TN","type":"education","lineage":["https://openalex.org/I8636806"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Mohamed Nazih Omri","raw_affiliation_strings":["University of Sousse,MARS Research Laboratory,Sousse,Tunisia","MARS Research Laboratory, University of Sousse, Sousse, Tunisia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Sousse,MARS Research Laboratory,Sousse,Tunisia","institution_ids":["https://openalex.org/I8636806"]},{"raw_affiliation_string":"MARS Research Laboratory, University of Sousse, Sousse, Tunisia","institution_ids":["https://openalex.org/I8636806"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4356,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64327073,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"754","last_page":"759"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.954200029373169,"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":0.954200029373169,"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":0.9034000039100647,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.8344098925590515},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.7717034220695496},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7010011672973633},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.608595609664917},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5910193920135498},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5887854695320129},{"id":"https://openalex.org/keywords/word-lists-by-frequency","display_name":"Word lists by frequency","score":0.5156306028366089},{"id":"https://openalex.org/keywords/tf\u2013idf","display_name":"tf\u2013idf","score":0.4497168958187103},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.39073804020881653},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.15251678228378296},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.12563669681549072},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.10654225945472717},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.04983878135681152}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.8344098925590515},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.7717034220695496},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7010011672973633},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.608595609664917},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5910193920135498},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5887854695320129},{"id":"https://openalex.org/C175293574","wikidata":"https://www.wikidata.org/wiki/Q697133","display_name":"Word lists by frequency","level":3,"score":0.5156306028366089},{"id":"https://openalex.org/C81758059","wikidata":"https://www.wikidata.org/wiki/Q796584","display_name":"tf\u2013idf","level":3,"score":0.4497168958187103},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.39073804020881653},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.15251678228378296},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.12563669681549072},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.10654225945472717},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.04983878135681152},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dese60595.2023.10468715","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dese60595.2023.10468715","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 16th International Conference on Developments in eSystems Engineering (DeSE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1481057431","https://openalex.org/W2473873820","https://openalex.org/W2627045032","https://openalex.org/W2770355807","https://openalex.org/W2785037737","https://openalex.org/W2790583645","https://openalex.org/W2889384752","https://openalex.org/W2907918833","https://openalex.org/W2947572693","https://openalex.org/W3000495517","https://openalex.org/W3005786652","https://openalex.org/W3045687124","https://openalex.org/W3124832753","https://openalex.org/W3136569803","https://openalex.org/W3176889653","https://openalex.org/W3200847954","https://openalex.org/W3210275149","https://openalex.org/W4285241330","https://openalex.org/W4322732127","https://openalex.org/W4322770133"],"related_works":["https://openalex.org/W3021501837","https://openalex.org/W2127709934","https://openalex.org/W1588516692","https://openalex.org/W2324052717","https://openalex.org/W2089917660","https://openalex.org/W2041122820","https://openalex.org/W1970935140","https://openalex.org/W3004397018","https://openalex.org/W1512024959","https://openalex.org/W2887719218"],"abstract_inverted_index":{"The":[0,102,119],"sentiment":[1,11,14],"mining":[2],"field":[3],"(also":[4],"known":[5],"as":[6],"opinion":[7,9],"mining,":[8,47],"extraction,":[10,15,87],"analysis":[12],"(SA),":[13],"and":[16,36,48,61,84,98,106,109],"so":[17],"on)":[18],"has":[19],"seen":[20],"significant":[21],"growth":[22],"in":[23,39],"academia.":[24],"Researchers":[25],"have":[26],"experimented":[27],"with":[28,80,129],"a":[29,57,65],"variety":[30],"of":[31,42,133],"approaches":[32],"to":[33,55,74,77,114,137],"automate":[34],"SA":[35],"other":[37],"fields":[38,41],"the":[40,116,125,138],"machine":[43],"learning":[44],"(ML),":[45],"data":[46],"natural":[49],"language":[50],"processing.":[51],"Our":[52],"research":[53],"aims":[54],"develop":[56],"model":[58],"that":[59],"extracts":[60],"categorizes":[62],"words":[63,79],"from":[64],"specific":[66],"text.":[67],"In":[68],"this":[69],"study,":[70],"we":[71],"used":[72,113],"TF-IDF":[73],"select":[75],"500":[76],"20,000":[78],"vector.":[81],"After":[82],"pre-processing":[83],"frequency-dependent":[85],"word":[86],"constructs":[88],"are":[89],"generated.":[90],"Four":[91],"Naive":[92,120],"Bayes":[93,121],"models":[94],"(complement,":[95],"multinomial,":[96],"Bernoulli,":[97],"Gaussian)":[99],"were":[100,112],"used.":[101],"kappa":[103],"scale,":[104],"precision":[105],"accuracy":[107,131],"scores,":[108],"F1":[110],"score":[111],"evaluate":[115],"proposed":[117],"model.":[118],"multinomial":[122],"system":[123],"produced":[124],"most":[126],"accurate":[127],"results,":[128],"an":[130],"rate":[132],"86.46":[134],"percent,":[135],"according":[136],"findings.":[139]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
