{"id":"https://openalex.org/W4200354659","doi":"https://doi.org/10.1109/cce53527.2021.9633111","title":"Effect of Rate of Change of Stock Prices with News Sentiment Analysis","display_name":"Effect of Rate of Change of Stock Prices with News Sentiment Analysis","publication_year":2021,"publication_date":"2021-11-10","ids":{"openalex":"https://openalex.org/W4200354659","doi":"https://doi.org/10.1109/cce53527.2021.9633111"},"language":"en","primary_location":{"id":"doi:10.1109/cce53527.2021.9633111","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cce53527.2021.9633111","pdf_url":null,"source":{"id":"https://openalex.org/S4363608599","display_name":"2021 18th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 18th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","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/A5056734192","display_name":"Sashank Sridhar","orcid":"https://orcid.org/0000-0001-6246-0772"},"institutions":[{"id":"https://openalex.org/I33585257","display_name":"Anna University, Chennai","ror":"https://ror.org/01qhf1r47","country_code":"IN","type":"education","lineage":["https://openalex.org/I33585257"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Sashank Sridhar","raw_affiliation_strings":["College of Engineering Guindy, Anna University,Department of Computer Science and Engineering,Chennai,India","Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India"],"affiliations":[{"raw_affiliation_string":"College of Engineering Guindy, Anna University,Department of Computer Science and Engineering,Chennai,India","institution_ids":["https://openalex.org/I33585257"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India","institution_ids":["https://openalex.org/I33585257"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057611626","display_name":"Sowmya Sanagavarapu","orcid":null},"institutions":[{"id":"https://openalex.org/I33585257","display_name":"Anna University, Chennai","ror":"https://ror.org/01qhf1r47","country_code":"IN","type":"education","lineage":["https://openalex.org/I33585257"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sowmya Sanagavarapu","raw_affiliation_strings":["College of Engineering Guindy, Anna University,Department of Computer Science and Engineering,Chennai,India","Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India"],"affiliations":[{"raw_affiliation_string":"College of Engineering Guindy, Anna University,Department of Computer Science and Engineering,Chennai,India","institution_ids":["https://openalex.org/I33585257"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India","institution_ids":["https://openalex.org/I33585257"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5056734192"],"corresponding_institution_ids":["https://openalex.org/I33585257"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20373027,"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":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998000264167786,"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":0.9998000264167786,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9886000156402588,"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"}},{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"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/random-forest","display_name":"Random forest","score":0.7303448915481567},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.7256125211715698},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6306546926498413},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.6273284554481506},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5286396145820618},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.5098101496696472},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.4896388649940491},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.4849715232849121},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.47917309403419495},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37757766246795654},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.29822084307670593},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.23442494869232178},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.21959152817726135}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7303448915481567},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7256125211715698},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6306546926498413},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.6273284554481506},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5286396145820618},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.5098101496696472},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.4896388649940491},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.4849715232849121},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.47917309403419495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37757766246795654},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29822084307670593},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.23442494869232178},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.21959152817726135},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C2780762169","wikidata":"https://www.wikidata.org/wiki/Q5905368","display_name":"Horse","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cce53527.2021.9633111","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cce53527.2021.9633111","pdf_url":null,"source":{"id":"https://openalex.org/S4363608599","display_name":"2021 18th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 18th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5400000214576721,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W2006724638","https://openalex.org/W2734706628","https://openalex.org/W2757242159","https://openalex.org/W2768950724","https://openalex.org/W2792466997","https://openalex.org/W2901852093","https://openalex.org/W2997159780","https://openalex.org/W3001790167","https://openalex.org/W3007075806","https://openalex.org/W3007191245","https://openalex.org/W3015981882","https://openalex.org/W3025207356","https://openalex.org/W3033786741","https://openalex.org/W3036345578","https://openalex.org/W3099457307","https://openalex.org/W3105750503","https://openalex.org/W3112932187","https://openalex.org/W3114272942","https://openalex.org/W3128596347","https://openalex.org/W3137083368","https://openalex.org/W3137111833","https://openalex.org/W3137184122","https://openalex.org/W3141951428","https://openalex.org/W3142751212","https://openalex.org/W3152486312","https://openalex.org/W3158636529","https://openalex.org/W3160511323","https://openalex.org/W3160627005","https://openalex.org/W3163930309","https://openalex.org/W3198550102","https://openalex.org/W4324013541","https://openalex.org/W6772923104","https://openalex.org/W6786011638"],"related_works":["https://openalex.org/W2548633793","https://openalex.org/W3013279174","https://openalex.org/W2941935829","https://openalex.org/W2596247554","https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3132372214","https://openalex.org/W3005992387","https://openalex.org/W4360924407"],"abstract_inverted_index":{"Mapping":[0],"the":[1,7,37,68,90,104,133,150],"variations":[2],"of":[3,18,39,41,43,93,100,103,132,139,147,163],"stock":[4,44,56],"prices":[5,45,94],"in":[6],"market":[8],"has":[9],"proven":[10],"to":[11,66,74,88],"be":[12],"a":[13,30,51,60,111,119,160],"challenge":[14],"to,":[15],"considering":[16],"impact":[17],"macroeconomic":[19],"factors,":[20],"such":[21],"as":[22,78],"news":[23,47,72,140],"headlines":[24,158],"on":[25],"them.":[26],"In":[27],"this":[28],"paper,":[29],"novel":[31],"framework":[32],"is":[33,126,142],"implemented":[34,87],"for":[35,59],"predicting":[36],"effect":[38],"rate":[40],"change":[42],"through":[46],"sentiment":[48,101,123,141],"by":[49],"using":[50],"standard":[52],"dataset":[53],"with":[54,96,118,130,144,156],"closing":[55],"price":[57,151],"rates":[58],"chosen":[61],"period.":[62],"Random":[63,120],"Forest":[64,121],"classifier":[65],"extract":[67],"sentiments":[69],"from":[70],"day-today":[71],"articles":[73],"identify":[75],"their":[76],"polarity":[77,138],"positive,":[79],"or":[80],"negative.":[81],"Bi-directional":[82],"Long":[83],"Short-Term":[84],"models":[85],"are":[86],"map":[89],"sliding":[91,98],"windows":[92,99],"along":[95],"corresponding":[97],"scores":[102],"articles.":[105],"Thus,":[106],"hybrid":[107],"architecture":[108],"that":[109],"combines":[110],"Bi-LSTM":[112],"based":[113,122],"time":[114],"series":[115],"prediction":[116,152],"model":[117,125,153],"analysis":[124],"developed":[127],"and":[128,149],"compared":[129],"some":[131],"existing":[134],"state-of-the-art":[135],"methodologies.":[136],"The":[137],"determined":[143],"an":[145],"accuracy":[146],"84.92%":[148],"performs":[154],"best":[155],"10":[157],"achieving":[159],"R-squared":[161],"score":[162],"74.84%.":[164]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}