{"id":"https://openalex.org/W3005866697","doi":"https://doi.org/10.1109/wocn45266.2019.8995164","title":"Extraction-Based Text Summarization and Sentiment Analysis of Online Reviews Using Hybrid Classification Method","display_name":"Extraction-Based Text Summarization and Sentiment Analysis of Online Reviews Using Hybrid Classification Method","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3005866697","doi":"https://doi.org/10.1109/wocn45266.2019.8995164","mag":"3005866697"},"language":"en","primary_location":{"id":"doi:10.1109/wocn45266.2019.8995164","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wocn45266.2019.8995164","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Sixteenth International Conference on Wireless and Optical Communication Networks (WOCN)","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/A5089853487","display_name":"Nisha Yadav","orcid":"https://orcid.org/0000-0003-3078-4398"},"institutions":[{"id":"https://openalex.org/I196622127","display_name":"Rajiv Gandhi Technical University","ror":"https://ror.org/03xmje391","country_code":"IN","type":"education","lineage":["https://openalex.org/I196622127"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nisha Yadav","raw_affiliation_strings":["B.Tech, CSE, LNCTS,Bhopal","B.Tech, CSE, LNCTS, Bhopal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"B.Tech, CSE, LNCTS,Bhopal","institution_ids":["https://openalex.org/I196622127"]},{"raw_affiliation_string":"B.Tech, CSE, LNCTS, Bhopal","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089779023","display_name":"Rajeev Kumar","orcid":"https://orcid.org/0000-0002-4141-1282"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rajeev Kumar","raw_affiliation_strings":["Professor, CSE, LNCTS"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Professor, CSE, LNCTS","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005033272","display_name":"Bhupesh Gour","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bhupesh Gour","raw_affiliation_strings":["Professor, CSE, LNCTS"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Professor, CSE, LNCTS","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054591491","display_name":"Asif Khan","orcid":"https://orcid.org/0000-0002-9840-5289"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Asif Ullah Khan","raw_affiliation_strings":["Professor, CSE, TITE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Professor, CSE, TITE","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5784,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.77132356,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998999834060669,"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":0.9998999834060669,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9970999956130981,"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":0.9868999719619751,"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/automatic-summarization","display_name":"Automatic summarization","score":0.935269832611084},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.821755051612854},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.8188566565513611},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5586839318275452},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5539973378181458},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5364416837692261},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47638458013534546},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.43698546290397644},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.4346035420894623},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4146125018596649}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.935269832611084},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.821755051612854},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.8188566565513611},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5586839318275452},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5539973378181458},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5364416837692261},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47638458013534546},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.43698546290397644},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.4346035420894623},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4146125018596649},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wocn45266.2019.8995164","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wocn45266.2019.8995164","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Sixteenth International Conference on Wireless and Optical Communication Networks (WOCN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W1572786359","https://openalex.org/W1964869363","https://openalex.org/W2078368227"],"related_works":["https://openalex.org/W2366403280","https://openalex.org/W1495108544","https://openalex.org/W2091301346","https://openalex.org/W3148229873","https://openalex.org/W4389760904","https://openalex.org/W2150160875","https://openalex.org/W4242223894","https://openalex.org/W4306886878","https://openalex.org/W2003578783","https://openalex.org/W2368651715"],"abstract_inverted_index":{"The":[0,249,266,284],"field":[1,44,304],"of":[2,12,69,75,87,108,122,139,143,164,191,198,202,210,247,270,274,277,288,305,311,330],"sentiment":[3,126,216,282,306,335],"mining":[4],"and":[5,15,33,37,47,60,78,116,176,212,218,240,262,286,323],"text":[6,51,151,187,219],"summarization":[7,142],"has":[8,26],"evoked":[9],"the":[10,18,23,41,84,88,106,141,144,150,186,189,196,199,275,278,289,303,328,331],"interest":[11,77],"many":[13,30],"scientists":[14],"researchers":[16],"over":[17],"last":[19],"few":[20],"years,":[21],"as":[22,57,95,136,154],"textual":[24],"data":[25,52,112,179],"become":[27],"useful":[28,158,178],"for":[29,45,124,170,281,334],"real-world":[31],"applications":[32],"challenges.":[34],"Sentiment":[35],"Analysis":[36],"Opinion":[38],"Mining":[39],"is":[40,152,167,193,230,243,299,314,321],"most":[42],"popular":[43],"analyzing":[46],"discovering":[48],"insights":[49],"from":[50,53,160,180],"various":[54],"sources,":[55],"such":[56],"Facebook,":[58],"Twitter":[59],"Amazon,":[61],"Zomato,":[62],"etc.":[63],"It":[64,166],"involves":[65],"a":[66,120,133,137,147,161,171,181,207,225,271,295],"computational":[67],"study":[68,273],"an":[70,96],"individual's":[71],"behavior":[72],"in":[73,105,302],"terms":[74,246],"buying":[76],"then":[79],"extracting":[80],"his":[81],"opinions":[82],"on":[83,132,195,215,233,327],"business":[85],"entity":[86,91],"company.":[89],"This":[90,204],"can":[92],"be":[93],"viewed":[94],"event,":[97],"individual,":[98],"blog":[99],"post":[100],"or":[101],"product":[102],"experience.":[103],"Scholars":[104],"fields":[107],"natural":[109],"language":[110],"processing,":[111],"mining,":[113],"machine":[114],"learning":[115],"others":[117],"have":[118],"tested":[119],"variety":[121],"methods":[123,236],"automating":[125],"analysis.":[127],"These":[128],"reviews":[129,145],"are":[130,292,325],"increasing":[131],"daily":[134],"basis,":[135],"result":[138],"which":[140,156],"plays":[146],"role":[148],"where":[149],"summarized":[153],"needed,":[155],"provides":[157,206],"information":[159],"large":[162,183],"number":[163],"reviews.":[165],"very":[168,182],"difficult":[169],"human":[172],"being":[173],"to":[174],"extract":[175],"interpret":[177],"file.":[184],"In":[185,221],"analysis,":[188],"value":[190],"sentences":[192],"decided":[194],"basis":[197],"linguistic":[200],"characteristics":[201],"sentences.":[203],"paper":[205],"comprehensive":[208],"review":[209,297],"current":[211],"past":[213],"work":[214,268],"analysis":[217,320],"description.":[220],"this":[222],"research":[223],"work,":[224],"new":[226],"hybrid":[227],"classification":[228,235],"system":[229],"proposed":[231,267,290],"based":[232],"coupling":[234],"using":[237,254],"arcing":[238],"classifiers":[239],"their":[241],"quality":[242],"evaluated":[244],"within":[245],"accuracy.":[248],"Classifier":[250],"Collection":[251],"was":[252],"constructed":[253],"Na\u00efve":[255],"Bayes":[256],"(NB),":[257],"Support":[258],"Vector":[259],"Machine":[260],"(SVM)":[261],"Genetic":[263],"Algorithm":[264],"(GA).":[265],"consists":[269],"comparative":[272,312],"efficacy":[276,329],"ensemble":[279,332],"technique":[280,333],"classification.":[283,307,336],"feasibility":[285],"benefits":[287],"approaches":[291],"demonstrated":[293],"by":[294],"restaurant":[296],"that":[298],"widely":[300],"used":[301],"A":[308],"wide":[309],"range":[310],"studies":[313],"performed":[315],"and,":[316],"ultimately,":[317],"some":[318],"in-depth":[319],"addressed":[322],"conclusions":[324],"drawn":[326]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}