{"id":"https://openalex.org/W2585092264","doi":"https://doi.org/10.1145/3003733.3003787","title":"Stock Price Forecasting via Sentiment Analysis on Twitter","display_name":"Stock Price Forecasting via Sentiment Analysis on Twitter","publication_year":2016,"publication_date":"2016-11-10","ids":{"openalex":"https://openalex.org/W2585092264","doi":"https://doi.org/10.1145/3003733.3003787","mag":"2585092264"},"language":"en","primary_location":{"id":"doi:10.1145/3003733.3003787","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3003733.3003787","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th Pan-Hellenic Conference on Informatics","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/A5053388303","display_name":"John Kordonis","orcid":null},"institutions":[{"id":"https://openalex.org/I147962203","display_name":"Democritus University of Thrace","ror":"https://ror.org/03bfqnx40","country_code":"GR","type":"education","lineage":["https://openalex.org/I147962203"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"John Kordonis","raw_affiliation_strings":["Electrical and Computer Engineering Dept., Democritus University of Thrace, Xanthi, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Dept., Democritus University of Thrace, Xanthi, Greece","institution_ids":["https://openalex.org/I147962203"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016041013","display_name":"Symeon Symeonidis","orcid":"https://orcid.org/0000-0002-3259-614X"},"institutions":[{"id":"https://openalex.org/I147962203","display_name":"Democritus University of Thrace","ror":"https://ror.org/03bfqnx40","country_code":"GR","type":"education","lineage":["https://openalex.org/I147962203"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Symeon Symeonidis","raw_affiliation_strings":["Electrical and Computer Engineering Dept., Democritus University of Thrace, Xanthi, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Dept., Democritus University of Thrace, Xanthi, Greece","institution_ids":["https://openalex.org/I147962203"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058891144","display_name":"Avi Arampatzis","orcid":"https://orcid.org/0000-0003-2415-4592"},"institutions":[{"id":"https://openalex.org/I147962203","display_name":"Democritus University of Thrace","ror":"https://ror.org/03bfqnx40","country_code":"GR","type":"education","lineage":["https://openalex.org/I147962203"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Avi Arampatzis","raw_affiliation_strings":["Electrical and Computer Engineering Dept., Democritus University of Thrace, Xanthi, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Dept., Democritus University of Thrace, Xanthi, Greece","institution_ids":["https://openalex.org/I147962203"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I147962203"],"apc_list":null,"apc_paid":null,"fwci":4.3384,"has_fulltext":false,"cited_by_count":63,"citation_normalized_percentile":{"value":0.94499564,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"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":0.9998999834060669,"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9973000288009644,"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"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.98089998960495,"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/sentiment-analysis","display_name":"Sentiment analysis","score":0.757426917552948},{"id":"https://openalex.org/keywords/contrarian","display_name":"Contrarian","score":0.7038328051567078},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6548304557800293},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6165056824684143},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.610625147819519},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.606417179107666},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5956604480743408},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5851388573646545},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.5836893916130066},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5509704947471619},{"id":"https://openalex.org/keywords/stock-market-prediction","display_name":"Stock market prediction","score":0.42125675082206726},{"id":"https://openalex.org/keywords/thriving","display_name":"Thriving","score":0.4177764058113098},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.39248576760292053},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.2703387439250946},{"id":"https://openalex.org/keywords/financial-economics","display_name":"Financial economics","score":0.25840312242507935},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.12844392657279968}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.757426917552948},{"id":"https://openalex.org/C2779652781","wikidata":"https://www.wikidata.org/wiki/Q5165738","display_name":"Contrarian","level":2,"score":0.7038328051567078},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6548304557800293},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6165056824684143},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.610625147819519},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.606417179107666},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5956604480743408},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5851388573646545},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.5836893916130066},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5509704947471619},{"id":"https://openalex.org/C2776256503","wikidata":"https://www.wikidata.org/wiki/Q7617906","display_name":"Stock market prediction","level":4,"score":0.42125675082206726},{"id":"https://openalex.org/C2776745293","wikidata":"https://www.wikidata.org/wiki/Q7798302","display_name":"Thriving","level":2,"score":0.4177764058113098},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.39248576760292053},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.2703387439250946},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.25840312242507935},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.12844392657279968},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","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},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"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/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3003733.3003787","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3003733.3003787","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th Pan-Hellenic Conference on Informatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W40549020","https://openalex.org/W1488781072","https://openalex.org/W1546721001","https://openalex.org/W2027860007","https://openalex.org/W2333981896","https://openalex.org/W2606882586","https://openalex.org/W2950974174"],"related_works":["https://openalex.org/W3110653194","https://openalex.org/W3123527334","https://openalex.org/W2165054589","https://openalex.org/W2116399990","https://openalex.org/W1964821925","https://openalex.org/W3209322685","https://openalex.org/W2316932825","https://openalex.org/W2625819229","https://openalex.org/W2382949348","https://openalex.org/W3122394886"],"abstract_inverted_index":{"Stock":[0],"price":[1,162],"forecasting":[2],"is":[3,61,188,223],"an":[4],"important":[5],"and":[6,16,69,115,130,240],"thriving":[7],"topic":[8],"in":[9,99,196],"financial":[10],"engineering":[11],"especially":[12],"since":[13],"new":[14],"techniques":[15,123],"approaches":[17],"on":[18,142,202],"this":[19,67,102,186],"matter":[20],"are":[21],"gaining":[22],"ground":[23],"constantly.":[24],"In":[25,101],"the":[26,29,43,46,54,74,78,117,143,149,182,192,197,208,218],"contemporary":[27],"era,":[28],"ceaseless":[30],"use":[31,70],"of":[32,56,77,94,119,185,205,220,238],"social":[33],"media":[34],"has":[35,40],"reached":[36],"unprecedented":[37],"levels,":[38],"which":[39,65,108],"led":[41],"to":[42,62,72,154,189,215,229],"belief":[44],"that":[45],"expressed":[47],"public":[48,95],"sentiment":[49,96,141,200,237],"could":[50],"be":[51,230],"correlated":[52],"with":[53,159,175],"behavior":[55,76],"stock":[57,80,160,241],"prices.":[58,81,242],"The":[59,225],"idea":[60],"recognize":[63],"patterns":[64],"confirm":[66],"correlation":[68,235],"them":[71,113],"predict":[73],"future":[75,198],"various":[79,120],"With":[82],"no":[83],"doubt,":[84],"though":[85],"uninteresting":[86],"individually,":[87],"tweets":[88,157,206,239],"can":[89],"provide":[90],"a":[91,106,137,203],"satisfactory":[92],"reflection":[93],"when":[97],"taken":[98],"aggregate.":[100],"paper,":[103],"we":[104,147,165,233],"develop":[105],"system":[107],"collects":[109],"past":[110,209],"tweets,":[111],"processes":[112],"further,":[114],"examines":[116],"effectiveness":[118],"machine":[121,151],"learning":[122,152],"such":[124],"as":[125,212,214,232],"Naive":[126],"Bayes":[127],"Bernoulli":[128],"classification":[129],"Support":[131],"Vector":[132],"Machine":[133],"(SVM),":[134],"for":[135],"providing":[136],"positive":[138],"or":[139],"negative":[140],"tweet":[144],"corpus.":[145],"Subsequently,":[146],"employ":[148],"same":[150],"algorithms":[153],"analyze":[155],"how":[156,191],"correlate":[158],"market":[161,193],"behavior.":[163],"Finally,":[164],"examine":[166,216],"our":[167,172],"prediction's":[168],"error":[169],"by":[170],"comparing":[171],"algorithm's":[173],"outcome":[174],"next":[176],"day's":[177],"actual":[178],"close":[179],"price.":[180],"Overall,":[181],"ultimate":[183],"goal":[184],"project":[187],"forecast":[190],"will":[194],"behave":[195],"via":[199],"analysis":[201],"set":[204],"over":[207],"few":[210],"days,":[211],"well":[213],"if":[217],"theory":[219],"contrarian":[221],"investing":[222],"applicable.":[224],"final":[226],"results":[227],"seem":[228],"promising":[231],"found":[234],"between":[236]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":16},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
