{"id":"https://openalex.org/W2898442926","doi":"https://doi.org/10.1108/el-04-2017-0075","title":"Capturing user sentiments for online Indian movie reviews","display_name":"Capturing user sentiments for online Indian movie reviews","publication_year":2018,"publication_date":"2018-10-25","ids":{"openalex":"https://openalex.org/W2898442926","doi":"https://doi.org/10.1108/el-04-2017-0075","mag":"2898442926"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1108/el-04-2017-0075","pdf_url":null,"source":{"id":"https://openalex.org/S902750600","display_name":"The Electronic Library","issn_l":"0264-0473","issn":["0264-0473","1758-616X"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-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/A5004696195","display_name":"Shrawan Kumar Trivedi","orcid":"https://orcid.org/0000-0001-7504-556X"},"institutions":[{"id":"https://openalex.org/I150870154","display_name":"Indian Institute of Management Ahmedabad","ror":"https://ror.org/02egcpy68","country_code":"IN","type":"funder","lineage":["https://openalex.org/I150870154"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shrawan Kumar Trivedi","raw_affiliation_strings":["Department of IT and Systems, Indian Institute of Management Sirmaur, Sirmaur, India"],"affiliations":[{"raw_affiliation_string":"Department of IT and Systems, Indian Institute of Management Sirmaur, Sirmaur, India","institution_ids":["https://openalex.org/I150870154"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103680647","display_name":"Shubhamoy Dey","orcid":null},"institutions":[{"id":"https://openalex.org/I33003672","display_name":"Indian Institute of Management Indore","ror":"https://ror.org/02j8pmw82","country_code":"IN","type":"education","lineage":["https://openalex.org/I33003672"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shubhamoy Dey","raw_affiliation_strings":["Department of Information Systems, Indian Institute of Management Indore, Indore, India"],"affiliations":[{"raw_affiliation_string":"Department of Information Systems, Indian Institute of Management Indore, Indore, India","institution_ids":["https://openalex.org/I33003672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082043453","display_name":"Anil Kumar","orcid":"https://orcid.org/0000-0002-1691-0098"},"institutions":[{"id":"https://openalex.org/I1323093577","display_name":"BML Munjal University","ror":"https://ror.org/058ay3j75","country_code":"IN","type":"education","lineage":["https://openalex.org/I1323093577"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anil Kumar","raw_affiliation_strings":["Department of Decision Science, BML Munjal University, Gurgaon, India"],"affiliations":[{"raw_affiliation_string":"Department of Decision Science, BML Munjal University, Gurgaon, India","institution_ids":["https://openalex.org/I1323093577"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.642,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":6,"citation_normalized_percentile":{"value":0.806755,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":80,"max":82},"biblio":{"volume":"36","issue":"4","first_page":"677","last_page":"695"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":1.0,"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":1.0,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9884,"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"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9763,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision 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":0.66030073},{"id":"https://openalex.org/keywords/information-gain-ratio","display_name":"Information gain ratio","score":0.58148897},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45247734}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.778834},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7601459},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7226677},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.72034824},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6720774},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.66030073},{"id":"https://openalex.org/C202185110","wikidata":"https://www.wikidata.org/wiki/Q6031086","display_name":"Information gain ratio","level":3,"score":0.58148897},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5239059},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.51156574},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4633724},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45247734},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3211254},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1108/el-04-2017-0075","pdf_url":null,"source":{"id":"https://openalex.org/S902750600","display_name":"The Electronic Library","issn_l":"0264-0473","issn":["0264-0473","1758-616X"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality education","score":0.84,"id":"https://metadata.un.org/sdg/4"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":52,"referenced_works":["https://openalex.org/W1190615618","https://openalex.org/W122553268","https://openalex.org/W142730124","https://openalex.org/W1481057431","https://openalex.org/W1485955713","https://openalex.org/W1573641422","https://openalex.org/W1808640434","https://openalex.org/W1924689489","https://openalex.org/W1979607625","https://openalex.org/W1984218333","https://openalex.org/W1996908850","https://openalex.org/W2011432097","https://openalex.org/W2021116776","https://openalex.org/W2063596712","https://openalex.org/W2065576748","https://openalex.org/W2096790918","https://openalex.org/W2096942889","https://openalex.org/W2097726431","https://openalex.org/W2104731083","https://openalex.org/W2108646579","https://openalex.org/W2109293916","https://openalex.org/W2109634664","https://openalex.org/W2111620038","https://openalex.org/W2114524997","https://openalex.org/W2115023510","https://openalex.org/W2121625413","https://openalex.org/W2126229897","https://openalex.org/W2132166479","https://openalex.org/W2141823131","https://openalex.org/W2143455647","https://openalex.org/W2148034183","https://openalex.org/W2149684865","https://openalex.org/W2155328222","https://openalex.org/W2160660844","https://openalex.org/W2161349318","https://openalex.org/W2163455955","https://openalex.org/W2164598857","https://openalex.org/W2166706824","https://openalex.org/W2168505588","https://openalex.org/W2169601064","https://openalex.org/W2185547049","https://openalex.org/W2250710744","https://openalex.org/W2272031392","https://openalex.org/W2293190740","https://openalex.org/W2404476957","https://openalex.org/W2516665893","https://openalex.org/W2530097699","https://openalex.org/W2553238562","https://openalex.org/W2566964269","https://openalex.org/W2621148318","https://openalex.org/W4211186029","https://openalex.org/W878442799"],"related_works":["https://openalex.org/W4386387568","https://openalex.org/W4378417285","https://openalex.org/W4315491841","https://openalex.org/W4295854500","https://openalex.org/W4291214310","https://openalex.org/W3130588842","https://openalex.org/W3021501837","https://openalex.org/W3003767271","https://openalex.org/W2915012007","https://openalex.org/W2914651671"],"abstract_inverted_index":{"Purpose":[0],"Sentiment":[1],"analysis":[2,25],"and":[3,15,36,56,86,90,100,115,151,206],"opinion":[4],"mining":[5],"are":[6],"emerging":[7],"areas":[8],"of":[9,26,71,121,130,164],"research":[10,20,199],"for":[11,126,160,176,211],"analyzing":[12],"Web":[13],"data":[14,203],"capturing":[16],"users\u2019":[17],"sentiments.":[18],"This":[19,195],"aims":[21],"to":[22,139,155,183],"present":[23],"sentiment":[24,212],"an":[27],"Indian":[28,201],"movie":[29],"review":[30,202],"corpus":[31,73],"using":[32,75,106],"natural":[33],"language":[34],"processing":[35],"various":[37],"machine":[38,50,59,177],"learning":[39,51,178],"classifiers.":[40],"Design/methodology/approach":[41],"In":[42],"this":[43,122],"paper,":[44],"a":[45,91,147,197,208],"comparative":[46,92],"study":[47,93,123],"between":[48,96],"three":[49],"classifiers":[52,65,99],"(Bayesian,":[53],"na\u00efve":[54],"Bayesian":[55,188],"support":[57],"vector":[58],"[SVM])":[60],"was":[61,94,137,167,174,181,190,215],"performed.":[62],"All":[63],"the":[64,69,72,127,132,141,157,161,172,187],"were":[66,104,204],"trained":[67],"on":[68],"words/features":[70],"extracted,":[74],"five":[76],"different":[77,107],"feature":[78,101,134],"selection":[79,102,135],"algorithms":[80],"(Chi-square,":[81],"info-gain,":[82],"gain":[83],"ratio,":[84],"one-R":[85,166],"relief-F":[87],"[RF]":[88],"attributes),":[89],"performed":[95,175],"them.":[97],"The":[98,119],"approaches":[103],"evaluated":[105],"metrics":[108],"(":[109],"F":[110,145],"-value,":[111],"false-positive":[112],"[FP]":[113],"rate":[114,150],"training":[116],"time).":[117],"Findings":[118],"results":[120],"show":[124],"that,":[125],"maximum":[128],"number":[129,163],"features,":[131,165],"RF":[133],"approach":[136],"found":[138,182],"be":[140,184],"best,":[142],"with":[143,192],"better":[144,168],"-values,":[146],"low":[148],"FP":[149],"less":[152],"time":[153],"needed":[154],"train":[156],"classifiers,":[158,179],"whereas":[159],"least":[162],"than":[169],"RF.":[170],"When":[171],"evaluation":[173],"SVM":[180],"superior,":[185],"although":[186],"classifier":[189],"comparable":[191],"SVM.":[193],"Originality/value":[194],"is":[196],"novel":[198],"where":[200],"collected":[205],"then":[207],"classification":[209],"model":[210],"polarity":[213],"(positive/negative)":[214],"constructed.":[216]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2898442926","counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-03-20T00:27:22.219117","created_date":"2018-11-02"}