{"id":"https://openalex.org/W4417336970","doi":"https://doi.org/10.1109/access.2025.3644013","title":"Can News Sentiment Improve Deep Learning Models for Nifty 50 Index Forecasting?","display_name":"Can News Sentiment Improve Deep Learning Models for Nifty 50 Index Forecasting?","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W4417336970","doi":"https://doi.org/10.1109/access.2025.3644013"},"language":null,"primary_location":{"id":"doi:10.1109/access.2025.3644013","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3644013","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3644013","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120807199","display_name":"Chaithra Sadashiva Kotekar","orcid":"https://orcid.org/0009-0003-6072-1707"},"institutions":[{"id":"https://openalex.org/I11880225","display_name":"National Institute of Technology Karnataka","ror":"https://ror.org/01hz4v948","country_code":"IN","type":"education","lineage":["https://openalex.org/I11880225"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Chaithra Sadashiva Kotekar","raw_affiliation_strings":["National Institute of Technology Karnataka, Surathkal, India"],"affiliations":[{"raw_affiliation_string":"National Institute of Technology Karnataka, Surathkal, India","institution_ids":["https://openalex.org/I11880225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032581304","display_name":"Biju R. Mohan","orcid":"https://orcid.org/0000-0002-3928-8924"},"institutions":[{"id":"https://openalex.org/I11880225","display_name":"National Institute of Technology Karnataka","ror":"https://ror.org/01hz4v948","country_code":"IN","type":"education","lineage":["https://openalex.org/I11880225"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Biju R. Mohan","raw_affiliation_strings":["National Institute of Technology Karnataka, Surathkal, India"],"affiliations":[{"raw_affiliation_string":"National Institute of Technology Karnataka, Surathkal, India","institution_ids":["https://openalex.org/I11880225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5120807200","display_name":"Vamsidhar Kolukuluri","orcid":null},"institutions":[{"id":"https://openalex.org/I11880225","display_name":"National Institute of Technology Karnataka","ror":"https://ror.org/01hz4v948","country_code":"IN","type":"education","lineage":["https://openalex.org/I11880225"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vamsidhar Kolukuluri","raw_affiliation_strings":["National Institute of Technology Karnataka, Surathkal, India"],"affiliations":[{"raw_affiliation_string":"National Institute of Technology Karnataka, Surathkal, India","institution_ids":["https://openalex.org/I11880225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5120807199"],"corresponding_institution_ids":["https://openalex.org/I11880225"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.47234707,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"2675","last_page":"2689"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9872000217437744,"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"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9872000217437744,"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"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.002099999925121665,"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.0015999999595806003,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6486999988555908},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6011999845504761},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.5784000158309937},{"id":"https://openalex.org/keywords/subjectivity","display_name":"Subjectivity","score":0.5418000221252441},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4027999937534332},{"id":"https://openalex.org/keywords/polarity","display_name":"Polarity (international relations)","score":0.399399995803833},{"id":"https://openalex.org/keywords/risk\u2013return-spectrum","display_name":"Risk\u2013return spectrum","score":0.39480000734329224},{"id":"https://openalex.org/keywords/index-fund","display_name":"Index fund","score":0.34529998898506165},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.3441999852657318}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.720300018787384},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6486999988555908},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6011999845504761},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5874000191688538},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.5784000158309937},{"id":"https://openalex.org/C202889954","wikidata":"https://www.wikidata.org/wiki/Q1139554","display_name":"Subjectivity","level":2,"score":0.5418000221252441},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4027999937534332},{"id":"https://openalex.org/C2777361361","wikidata":"https://www.wikidata.org/wiki/Q1112585","display_name":"Polarity (international relations)","level":3,"score":0.399399995803833},{"id":"https://openalex.org/C30755413","wikidata":"https://www.wikidata.org/wiki/Q1245402","display_name":"Risk\u2013return spectrum","level":3,"score":0.39480000734329224},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3772999942302704},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36320000886917114},{"id":"https://openalex.org/C6243943","wikidata":"https://www.wikidata.org/wiki/Q1195348","display_name":"Index fund","level":5,"score":0.34529998898506165},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3441999852657318},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.3228999972343445},{"id":"https://openalex.org/C88389905","wikidata":"https://www.wikidata.org/wiki/Q223371","display_name":"Stock market index","level":4,"score":0.32170000672340393},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C2778865806","wikidata":"https://www.wikidata.org/wiki/Q6060850","display_name":"Investment decisions","level":3,"score":0.2994999885559082},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C27548731","wikidata":"https://www.wikidata.org/wiki/Q88272","display_name":"Investment (military)","level":3,"score":0.2872999906539917},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C103144560","wikidata":"https://www.wikidata.org/wiki/Q2670999","display_name":"Investment strategy","level":3,"score":0.2757999897003174},{"id":"https://openalex.org/C76073288","wikidata":"https://www.wikidata.org/wiki/Q1337875","display_name":"Financial risk","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2727999985218048},{"id":"https://openalex.org/C169549615","wikidata":"https://www.wikidata.org/wiki/Q939134","display_name":"Return on investment","level":3,"score":0.272599995136261},{"id":"https://openalex.org/C2987525970","wikidata":"https://www.wikidata.org/wiki/Q96374569","display_name":"Causal analysis","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2025.3644013","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3644013","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3644013","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3644013","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W2547440287","https://openalex.org/W2969912626","https://openalex.org/W2971724044","https://openalex.org/W3011387630","https://openalex.org/W3107642158","https://openalex.org/W3122430099","https://openalex.org/W3122799874","https://openalex.org/W3125952890","https://openalex.org/W3135361293","https://openalex.org/W3148222858","https://openalex.org/W3176439250","https://openalex.org/W3181448069","https://openalex.org/W3190912843","https://openalex.org/W3214860080","https://openalex.org/W4210696029","https://openalex.org/W4210764489","https://openalex.org/W4220945596","https://openalex.org/W4229333499","https://openalex.org/W4251074844","https://openalex.org/W4283317341","https://openalex.org/W4289656276","https://openalex.org/W4296209180","https://openalex.org/W4298110867","https://openalex.org/W4309947347","https://openalex.org/W4311425916","https://openalex.org/W4312102719","https://openalex.org/W4319430877","https://openalex.org/W4327568915","https://openalex.org/W4366996344","https://openalex.org/W4382175553","https://openalex.org/W4382793581","https://openalex.org/W4386255954","https://openalex.org/W4389289219","https://openalex.org/W4389739264","https://openalex.org/W4389795458","https://openalex.org/W4390674636","https://openalex.org/W4391754867","https://openalex.org/W4392916115","https://openalex.org/W4394978523","https://openalex.org/W4396878152","https://openalex.org/W4399528708","https://openalex.org/W4400275479","https://openalex.org/W4400698552","https://openalex.org/W4401607596","https://openalex.org/W4401924105","https://openalex.org/W4402351715","https://openalex.org/W4402968940","https://openalex.org/W4405097993","https://openalex.org/W4405560120","https://openalex.org/W4407901049","https://openalex.org/W4408160114","https://openalex.org/W4409707942","https://openalex.org/W4410196869","https://openalex.org/W4410393726","https://openalex.org/W4410609043"],"related_works":[],"abstract_inverted_index":{"A":[0],"stock":[1],"index,":[2],"such":[3],"as":[4,36,38],"the":[5,13,47,70,114,123,160],"Nifty":[6],"50,":[7],"offers":[8],"diversified":[9],"exposure":[10],"and":[11,27,33,46,65,76,94,105,138,156,168,194,203],"reduces":[12],"risk":[14,202],"of":[15,72],"investing":[16],"in":[17,133],"individual":[18],"companies.":[19],"Index":[20],"price":[21],"movements":[22,64],"are":[23,78,99],"influenced":[24],"by":[25],"internal":[26],"external":[28],"factors,":[29],"including":[30,90],"political,":[31],"economic,":[32],"environmental":[34],"developments,":[35],"well":[37],"historical":[39,139],"trends.":[40],"The":[41,186],"relationship":[42],"between":[43],"news":[44,60,82],"sentiment":[45,61,67,154],"Nifty50":[48],"return":[49,102,117,140],"has":[50,188],"not":[51],"been":[52],"thoroughly":[53],"studied.":[54],"This":[55],"study":[56,187],"examines":[57],"whether":[58],"financial":[59,81],"affects":[62],"index":[63,116],"how":[66],"can":[68],"enhance":[69],"prediction":[71],"next-day":[73,115],"returns.":[74],"Polarity":[75],"subjectivity":[77,106,183],"extracted":[79],"from":[80],"using":[83,107],"pre-trained":[84],"transformer":[85],"models.":[86,146],"Deep":[87],"learning":[88],"models,":[89],"LSTM,":[91],"GRU,":[92],"SimpleRNN,":[93],"temporal":[95],"Kolmogorov-Arnold":[96],"network":[97],"(TKAN),":[98],"trained":[100],"on":[101,180],"sign,":[103],"polarity,":[104],"a":[108,130,149,157,177],"five-day":[109],"rolling":[110],"window":[111],"to":[112,200],"forecast":[113],"sign.":[118],"Experimental":[119],"results":[120],"demonstrate":[121],"that":[122,174],"proposed":[124],"approach":[125],"outperforms":[126],"baseline":[127],"methods,":[128],"achieving":[129],"5.2%":[131],"improvement":[132],"average":[134],"accuracy.":[135,165],"Incorporating":[136],"polarity":[137,175],"signs":[141],"enhances":[142],"performance":[143],"across":[144],"all":[145],"By":[147],"employing":[148],"focused":[150],"feature":[151],"set,":[152],"domain-specific":[153],"analysis,":[155],"streamlined":[158],"architecture,":[159],"model":[161],"achieves":[162],"superior":[163],"predictive":[164],"Causal":[166],"analysis":[167],"Shapley":[169],"Additive":[170],"Explanations":[171],"(SHAP)":[172],"reveal":[173],"exhibits":[176],"causal":[178],"effect":[179],"returns,":[181],"while":[182],"does":[184],"not.":[185],"practical":[189],"significance,":[190],"offering":[191],"day":[192],"traders":[193],"short-term":[195],"investors":[196],"timely,":[197],"data-driven":[198],"insights":[199],"manage":[201],"make":[204],"informed":[205],"investment":[206],"choices.":[207]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-12-15T00:00:00"}