{"id":"https://openalex.org/W3159521331","doi":"https://doi.org/10.14704/web/v18i1/web18097","title":"Understanding Consumer Product Sentiments through Supervised Models on Cloud: Pre and Post COVID","display_name":"Understanding Consumer Product Sentiments through Supervised Models on Cloud: Pre and Post COVID","publication_year":2021,"publication_date":"2021-04-29","ids":{"openalex":"https://openalex.org/W3159521331","doi":"https://doi.org/10.14704/web/v18i1/web18097","mag":"3159521331"},"language":"en","primary_location":{"id":"doi:10.14704/web/v18i1/web18097","is_oa":true,"landing_page_url":"https://doi.org/10.14704/web/v18i1/web18097","pdf_url":null,"source":{"id":"https://openalex.org/S4210195749","display_name":"Webology","issn_l":"1735-188X","issn":["1735-188X"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310318996","host_organization_name":"University of Tehran Press","host_organization_lineage":["https://openalex.org/P4310318996"],"host_organization_lineage_names":["University of Tehran Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Webology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.14704/web/v18i1/web18097","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103215020","display_name":"Abhishek Gupta","orcid":"https://orcid.org/0000-0002-3814-9084"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Abhishek Gupta","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027342905","display_name":"Dwijendra Nath Dwivedi","orcid":"https://orcid.org/0000-0001-7662-415X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dwijendra Nath Dwivedi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059202448","display_name":"Jigar Shah","orcid":"https://orcid.org/0000-0002-9375-528X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jigar Shah","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5113962118","display_name":"Ravi Saroj","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ravi Saroj","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103215020"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.3785,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.90445993,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"18","issue":"1","first_page":"406","last_page":"415"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9993000030517578,"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":0.9993000030517578,"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":0.994700014591217,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9761999845504761,"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":0.813681960105896},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6762824058532715},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.6379603743553162},{"id":"https://openalex.org/keywords/service","display_name":"Service (business)","score":0.5959427356719971},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.5894262790679932},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.5820774435997009},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.508091926574707},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.47933492064476013},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4763736426830292},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.4714300036430359},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.4508473873138428},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44756028056144714},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36440640687942505},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.22316670417785645},{"id":"https://openalex.org/keywords/marketing","display_name":"Marketing","score":0.13909974694252014},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.12326964735984802},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.09318408370018005}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.813681960105896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6762824058532715},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.6379603743553162},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.5959427356719971},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5894262790679932},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.5820774435997009},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.508091926574707},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.47933492064476013},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4763736426830292},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.4714300036430359},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.4508473873138428},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44756028056144714},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36440640687942505},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.22316670417785645},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.13909974694252014},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.12326964735984802},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.09318408370018005},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14704/web/v18i1/web18097","is_oa":true,"landing_page_url":"https://doi.org/10.14704/web/v18i1/web18097","pdf_url":null,"source":{"id":"https://openalex.org/S4210195749","display_name":"Webology","issn_l":"1735-188X","issn":["1735-188X"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310318996","host_organization_name":"University of Tehran Press","host_organization_lineage":["https://openalex.org/P4310318996"],"host_organization_lineage_names":["University of Tehran Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Webology","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.14704/web/v18i1/web18097","is_oa":true,"landing_page_url":"https://doi.org/10.14704/web/v18i1/web18097","pdf_url":null,"source":{"id":"https://openalex.org/S4210195749","display_name":"Webology","issn_l":"1735-188X","issn":["1735-188X"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310318996","host_organization_name":"University of Tehran Press","host_organization_lineage":["https://openalex.org/P4310318996"],"host_organization_lineage_names":["University of Tehran Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Webology","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5099999904632568}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W2295738202","https://openalex.org/W2569242798"],"related_works":["https://openalex.org/W2389818373","https://openalex.org/W2220831889","https://openalex.org/W2056226831","https://openalex.org/W3013312691","https://openalex.org/W3027421045","https://openalex.org/W4312683641","https://openalex.org/W2576320324","https://openalex.org/W2980386803","https://openalex.org/W3215994059","https://openalex.org/W2319823519"],"abstract_inverted_index":{"While":[0],"a":[1,94],"lot":[2],"of":[3,16,50,58,81,93,122,150],"work":[4],"is":[5,18,54,73,84],"done":[6,55],"on":[7,20,30,86,157],"extracting":[8],"sentiments":[9],"and":[10,24,61,125],"opinions":[11],"in":[12,100,120,128],"unstructured":[13],"text,":[14],"majority":[15],"it":[17,72],"focused":[19,29,85],"contextual":[21,37],"sentiment":[22,49],"mining":[23],"features":[25,45],"that":[26,46,89,133,179],"are":[27,105],"more":[28],"sentiments.":[31],"The":[32,78],"team":[33,153],"attempted":[34],"to":[35,40,136,159,181],"use":[36],"text":[38],"analytics":[39],"identify":[41],"product":[42,67,95],"or":[43,68,96],"service":[44,69],"drives":[47],"the":[48,51,76,82,117,148,152,161,170,177],"user.":[52],"This":[53],"through":[56],"application":[57],"cosine":[59],"similarity":[60],"neural":[62],"networks.":[63],"Customers":[64],"speak":[65],"about":[66],"feature":[70],"when":[71],"important":[74],"for":[75,165],"them.":[77],"second":[79],"stage":[80],"analysis":[83],"supervised":[87],"learning,":[88],"identifies":[90],"key":[91],"drivers":[92],"service.":[97],"It":[98],"helps":[99,174],"deriving":[101],"those":[102],"elements":[103],"which":[104],"subconsciously":[106],"being":[107],"evaluated":[108],"by":[109,141,184],"customers":[110],"but":[111,143],"not":[112,144],"spoken.":[113],"We":[114,131],"also":[115],"test":[116],"significant":[118],"difference":[119],"views":[121],"people":[123],"pre":[124],"post":[126,171],"Covid":[127,137,172],"their":[129],"reviews.":[130],"found":[132],"factors":[134],"related":[135],"have":[138],"gone":[139],"up":[140],"30%":[142],"statistically":[145],"significant.":[146],"Given":[147],"volume":[149],"data,":[151],"has":[154],"analyzed":[155],"these":[156],"cloud":[158,162],"assess":[160],"computing":[163],"readiness":[164],"such":[166],"analysis.":[167],"Feedback":[168],"around":[169],"topics":[173],"us":[175],"understand":[176],"issues":[178],"need":[180],"be":[182],"addressed":[183],"restaurant":[185],"industry.":[186]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
