{"id":"https://openalex.org/W4388938011","doi":"https://doi.org/10.1109/icccnt56998.2023.10306670","title":"PSO-tuned support vector classifier based Nifty50 index movement prediction using market positions and sentiment scores","display_name":"PSO-tuned support vector classifier based Nifty50 index movement prediction using market positions and sentiment scores","publication_year":2023,"publication_date":"2023-07-06","ids":{"openalex":"https://openalex.org/W4388938011","doi":"https://doi.org/10.1109/icccnt56998.2023.10306670"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt56998.2023.10306670","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10306670","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","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/A5048254229","display_name":"Deepshi Garg","orcid":"https://orcid.org/0000-0002-0997-4333"},"institutions":[{"id":"https://openalex.org/I933318745","display_name":"Dehradun Institute of Technology University","ror":"https://ror.org/01v5k4d73","country_code":"IN","type":"education","lineage":["https://openalex.org/I933318745"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Deepshi Garg","raw_affiliation_strings":["DIT University,School of Liberal Arts and Management,Dehradun,India,248009"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DIT University,School of Liberal Arts and Management,Dehradun,India,248009","institution_ids":["https://openalex.org/I933318745"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100693722","display_name":"Rakesh Kumar Pandey","orcid":"https://orcid.org/0000-0002-5845-094X"},"institutions":[{"id":"https://openalex.org/I933318745","display_name":"Dehradun Institute of Technology University","ror":"https://ror.org/01v5k4d73","country_code":"IN","type":"education","lineage":["https://openalex.org/I933318745"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rakesh Kumar Pandey","raw_affiliation_strings":["DIT University,School of Engineering and Technology,Dehradun,India,248009"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DIT University,School of Engineering and Technology,Dehradun,India,248009","institution_ids":["https://openalex.org/I933318745"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014801280","display_name":"Prakash Tiwari","orcid":"https://orcid.org/0000-0002-1915-6756"},"institutions":[{"id":"https://openalex.org/I933318745","display_name":"Dehradun Institute of Technology University","ror":"https://ror.org/01v5k4d73","country_code":"IN","type":"education","lineage":["https://openalex.org/I933318745"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Prakash Tiwari","raw_affiliation_strings":["DIT University,School of Liberal Arts and Management,Dehradun,India,248009"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DIT University,School of Liberal Arts and Management,Dehradun,India,248009","institution_ids":["https://openalex.org/I933318745"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2028822,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998999834060669,"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":0.9998999834060669,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9851999878883362,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7290356755256653},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5868780612945557},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.5776693820953369},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.5743640065193176},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5546627044677734},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5492194294929504},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5487565994262695},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.5311132073402405},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.49772360920906067},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.49706438183784485},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4287700951099396},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3642849326133728},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.2767754793167114}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7290356755256653},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5868780612945557},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.5776693820953369},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.5743640065193176},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5546627044677734},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5492194294929504},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5487565994262695},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.5311132073402405},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.49772360920906067},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.49706438183784485},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4287700951099396},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3642849326133728},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2767754793167114},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt56998.2023.10306670","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt56998.2023.10306670","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1986187360","https://openalex.org/W2007358469","https://openalex.org/W2042816221","https://openalex.org/W2109364787","https://openalex.org/W2333080944","https://openalex.org/W2464243336","https://openalex.org/W2604913812","https://openalex.org/W2886249837","https://openalex.org/W2936404407","https://openalex.org/W3011387630","https://openalex.org/W3094317011","https://openalex.org/W3120269867","https://openalex.org/W3125082527","https://openalex.org/W3209191650","https://openalex.org/W3216239731","https://openalex.org/W4200601632","https://openalex.org/W4239806463","https://openalex.org/W4283698080","https://openalex.org/W4360948923","https://openalex.org/W6696899594","https://openalex.org/W6752042080"],"related_works":["https://openalex.org/W2140186469","https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W4389724018","https://openalex.org/W4318719684","https://openalex.org/W3183136280","https://openalex.org/W4318559728","https://openalex.org/W2775233965","https://openalex.org/W4360995913","https://openalex.org/W4312193868"],"abstract_inverted_index":{"Predicting":[0],"the":[1,4,28,53,60,64,93,99,137,142,156],"movement":[2],"of":[3,30,55,63,98,140],"stock":[5,31],"price":[6,62],"index":[7,165],"is":[8,38,159],"a":[9],"challenging":[10],"financial":[11],"time":[12],"series":[13],"analysis":[14],"task.":[15],"However,":[16],"an":[17,48],"accurate":[18,35],"forecast":[19,52,164],"may":[20],"generate":[21],"enormous":[22],"profits":[23],"for":[24,82],"investors.":[25],"Due":[26],"to":[27,46,51,91,125,163],"complexity":[29],"market":[32,86,144],"data,":[33],"developing":[34],"prediction":[36,94,130,153],"models":[37],"arduous.":[39],"In":[40],"this":[41,43],"context,":[42],"study":[44],"aims":[45],"develop":[47],"effective":[49],"model":[50,158],"direction":[54],"daily":[56],"returns":[57],"based":[58],"on":[59],"closing":[61],"Nifty50.":[65],"The":[66,85,118],"data":[67],"from":[68],"multiple":[69],"social":[70,147],"media":[71,148],"platforms,":[72],"including":[73,104],"Twitter,":[74],"StockTwits,":[75],"Facebook,":[76],"and":[77,108,155,161],"YouTube,":[78],"have":[79,88,121],"been":[80,89,116,122,134],"combined":[81],"sentiment":[83],"analysis.":[84],"positions":[87,145],"used":[90],"train":[92],"model.":[95],"Performance":[96],"comparison":[97],"supervised":[100],"machine":[101],"learning":[102],"models,":[103],"support":[105,112],"vector":[106,113],"classifier":[107],"particle":[109],"swarm":[110],"optimization-assisted":[111],"classifier,":[114],"has":[115,133],"conducted.":[117],"model's":[119],"hyperparameters":[120],"carefully":[123],"tuned":[124],"attain":[126],"more":[127],"than":[128],"99%":[129],"accuracy.":[131],"It":[132],"observed":[135],"that":[136],"novel":[138],"approach":[139],"combining":[141],"Nifty50":[143],"with":[146],"sentiments":[149],"resulted":[150],"in":[151],"high":[152],"performance,":[154],"proposed":[157],"robust":[160],"dependable":[162],"returns.":[166]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
