{"id":"https://openalex.org/W4416301746","doi":"https://doi.org/10.1186/s40537-025-01315-2","title":"Natural language processing for extracting consumer sentiment dynamics through multimodal social media analysis to predict microeconomic consumption pattern shifts","display_name":"Natural language processing for extracting consumer sentiment dynamics through multimodal social media analysis to predict microeconomic consumption pattern shifts","publication_year":2025,"publication_date":"2025-11-17","ids":{"openalex":"https://openalex.org/W4416301746","doi":"https://doi.org/10.1186/s40537-025-01315-2"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-025-01315-2","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01315-2","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01315-2","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01315-2","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yukai Weng","orcid":null},"institutions":[{"id":"https://openalex.org/I2800556661","display_name":"China Tobacco","ror":"https://ror.org/030d08e08","country_code":"CN","type":"government","lineage":["https://openalex.org/I2800556661"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yukai Weng","raw_affiliation_strings":["Shanghai Tobacco Group Co., Ltd, Shanghai, 200082, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Tobacco Group Co., Ltd, Shanghai, 200082, China","institution_ids":["https://openalex.org/I2800556661"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043637729","display_name":"Haytham F. Isleem","orcid":"https://orcid.org/0000-0002-4826-7420"},"institutions":[{"id":"https://openalex.org/I52099693","display_name":"University of York","ror":"https://ror.org/04m01e293","country_code":"GB","type":"education","lineage":["https://openalex.org/I52099693"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Haytham F. Isleem","raw_affiliation_strings":["Department of computer Science, University of York, No. 588 Jiushui East Rd., YORK, YO10 5DD, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Department of computer Science, University of York, No. 588 Jiushui East Rd., YORK, YO10 5DD, United Kingdom","institution_ids":["https://openalex.org/I52099693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079481884","display_name":"Khalil El Hindi","orcid":"https://orcid.org/0000-0003-2457-9961"},"institutions":[{"id":"https://openalex.org/I28022161","display_name":"King Saud University","ror":"https://ror.org/02f81g417","country_code":"SA","type":"education","lineage":["https://openalex.org/I28022161"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Khalil El Hindi","raw_affiliation_strings":["Department of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia","institution_ids":["https://openalex.org/I28022161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060873940","display_name":"Absalom E. Ezugwu","orcid":"https://orcid.org/0000-0002-3721-3400"},"institutions":[{"id":"https://openalex.org/I27765905","display_name":"North-West University","ror":"https://ror.org/010f1sq29","country_code":"ZA","type":"education","lineage":["https://openalex.org/I27765905"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Absalom E. Ezugwu","raw_affiliation_strings":["Unit for Data Science and Computing, North-West University, 11, Hofman Street, Potchefstroom, 2520, South Africa"],"affiliations":[{"raw_affiliation_string":"Unit for Data Science and Computing, North-West University, 11, Hofman Street, Potchefstroom, 2520, South Africa","institution_ids":["https://openalex.org/I27765905"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5043637729"],"corresponding_institution_ids":["https://openalex.org/I52099693"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":2.2283,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.91461165,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"12","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.2538999915122986,"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":0.2538999915122986,"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":0.09449999779462814,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.08540000021457672,"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":0.7929999828338623},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.6226999759674072},{"id":"https://openalex.org/keywords/consumption","display_name":"Consumption (sociology)","score":0.579800009727478},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5098000168800354},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.503000020980835},{"id":"https://openalex.org/keywords/social-media-analytics","display_name":"Social media analytics","score":0.4593999981880188},{"id":"https://openalex.org/keywords/predictive-analytics","display_name":"Predictive analytics","score":0.41269999742507935},{"id":"https://openalex.org/keywords/lagging","display_name":"Lagging","score":0.39739999175071716},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.35899999737739563}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7929999828338623},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7827000021934509},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.6226999759674072},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.579800009727478},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5098000168800354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5076000094413757},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.503000020980835},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4713999927043915},{"id":"https://openalex.org/C2778729106","wikidata":"https://www.wikidata.org/wiki/Q1140126","display_name":"Social media analytics","level":3,"score":0.4593999981880188},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4180999994277954},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.41269999742507935},{"id":"https://openalex.org/C2776962539","wikidata":"https://www.wikidata.org/wiki/Q6472078","display_name":"Lagging","level":2,"score":0.39739999175071716},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35899999737739563},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35190001130104065},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.34450000524520874},{"id":"https://openalex.org/C23213687","wikidata":"https://www.wikidata.org/wiki/Q301468","display_name":"Consumer behaviour","level":2,"score":0.34060001373291016},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C177033891","wikidata":"https://www.wikidata.org/wiki/Q5164722","display_name":"Consumer spending","level":3,"score":0.314300000667572},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.29109999537467957},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.29030001163482666},{"id":"https://openalex.org/C2778660142","wikidata":"https://www.wikidata.org/wiki/Q6805591","display_name":"Media consumption","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2660999894142151},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C17632256","wikidata":"https://www.wikidata.org/wiki/Q1076968","display_name":"Digital media","level":2,"score":0.25949999690055847}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s40537-025-01315-2","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01315-2","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01315-2","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f525191fd2f34b81b8e0999bd6da2ed0","is_oa":true,"landing_page_url":"https://doaj.org/article/f525191fd2f34b81b8e0999bd6da2ed0","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 12, Iss 1, Pp 1-31 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-025-01315-2","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01315-2","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01315-2","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6914937412","display_name":null,"funder_award_id":"ORF-2025-953","funder_id":"https://openalex.org/F4320321145","funder_display_name":"King Saud University"}],"funders":[{"id":"https://openalex.org/F4320321145","display_name":"King Saud University","ror":"https://ror.org/02f81g417"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416301746.pdf","grobid_xml":"https://content.openalex.org/works/W4416301746.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W3019577029","https://openalex.org/W3048195943","https://openalex.org/W3090455890","https://openalex.org/W3126284633","https://openalex.org/W3128562462","https://openalex.org/W3190316647","https://openalex.org/W4313550068","https://openalex.org/W4327937802","https://openalex.org/W4376472913","https://openalex.org/W4385573966","https://openalex.org/W4387807044","https://openalex.org/W4389524495","https://openalex.org/W4391577343","https://openalex.org/W4393271773","https://openalex.org/W4395053551","https://openalex.org/W4399146372","https://openalex.org/W4399537055","https://openalex.org/W4401174760","https://openalex.org/W4402024747","https://openalex.org/W4402618489","https://openalex.org/W4403616295","https://openalex.org/W4405419173","https://openalex.org/W4408985677","https://openalex.org/W4412155125"],"related_works":[],"abstract_inverted_index":{"Traditional":[0],"economic":[1],"predictions":[2],"rely":[3],"on":[4,63,117,224],"lagging":[5],"indicators":[6,232],"that":[7,42,154,198,222],"miss":[8],"the":[9,192,206,220,230,234],"early":[10],"signals":[11],"of":[12,22,96,98,109,135,176,229,233],"change":[13],"in":[14,68,128,142,275],"consumption":[15,118,129,178,249],"patterns,":[16],"effectively":[17],"robbing":[18],"businesses":[19],"and":[20,47,79,113,131,138,159,163,208,236],"policymakers":[21],"strategic":[23],"vision.":[24],"This":[25],"study":[26],"seeks":[27],"to":[28,50,90,103,191,213],"fill":[29],"this":[30,214],"significant":[31],"gap":[32],"by":[33,269],"developing":[34],"SENTIMENT-ECON,":[35],"a":[36,150],"microeconomic":[37],"modeling":[38],"framework":[39,125],"for":[40,173],"prediction":[41],"applies":[43],"natural":[44],"language":[45],"processing":[46],"multimodal":[48],"analytics":[49],"extract":[51],"consumer":[52],"sentiment":[53,161,194,223,247],"dynamics":[54],"from":[55,65],"social":[56,225],"media":[57,226,246],"sites.":[58],"We":[59,71,148],"use":[60,149],"architectures":[61],"based":[62],"transformers":[64],"deep":[66],"learning":[67],"time-series":[69],"forecasting.":[70],"establish":[72],"causal":[73],"relationships":[74],"between":[75],"digital":[76],"expression":[77],"patterns":[78,130],"subsequent":[80],"actual":[81],"market":[82,122],"behavior.":[83],"Our":[84],"SENTIMENT-ECON":[85],"neural":[86],"architecture":[87],"allows":[88,172],"us":[89],"apply":[91],"over":[92],"five":[93],"years":[94],"(2018\u20132023)":[95],"analysis":[97,255],"17.3":[99],"million":[100],"multi-platform":[101],"posts":[102],"demonstrate":[104],"an":[105],"unprecedented":[106],"predictive":[107],"accuracy":[108],"RMSE":[110],"=":[111,115],"0.031":[112],"MAPE":[114],"2.7%":[116],"pattern":[119],"shifts":[120,250],"across":[121],"segments.":[123],"The":[124],"predicted":[126],"changes":[127],"3.8":[132,251],"weeks":[133,252],"ahead":[134],"conventional":[136],"indicators,":[137],"was":[139],"particularly":[140],"successful":[141],"discretionary":[143,277],"categories":[144],"(94.2%":[145],"success":[146],"rate).":[147],"bidirectional":[151],"attention":[152],"mechanism":[153],"identifies":[155],"how":[156],"text,":[157],"image":[158],"video":[160],"overlap":[162],"reinforce":[164],"one":[165,228],"another.":[166],"A":[167],"temporal":[168],"convolutional":[169],"network":[170],"(TCN)":[171],"improved":[174],"detection":[175],"new":[177],"trends":[179],"before":[180],"they":[181],"arise":[182],"within":[183],"traditional":[184],"datasets.":[185],"When":[186],"we":[187,196],"compare":[188],"our":[189],"method":[190],"well-known":[193],"indices,":[195],"find":[197],"it":[199,216,237],"is":[200],"37%":[201],"more":[202],"accurate":[203],"at":[204],"predicting":[205],"future":[207],"also":[209],"42%":[210],"faster.":[211],"According":[212],"research,":[215],"may":[217],"well":[218],"be":[219,239],"case":[221],"represents":[227],"best":[231],"economy,":[235],"should":[238],"incorporated":[240],"into":[241],"BIs":[242],"or":[243],"SCs.":[244],"Social":[245],"predicts":[248],"early.":[253],"Multimodal":[254],"(text":[256],"+":[257,259],"images":[258],"videos)":[260],"outperforms":[261],"text-only":[262],"models.":[263],"Quantum-inspired":[264],"fusion":[265],"improves":[266],"sentiment-behavior":[267],"correlation":[268],"35.5%.":[270],"Framework":[271],"achieves":[272],"94.2%":[273],"F1-Score":[274],"detecting":[276],"spending":[278],"shifts.":[279],"Causal":[280],"mapping":[281],"reveals":[282],"platform-specific":[283],"behavioral":[284],"signals.":[285]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-11-17T00:00:00"}