{"id":"https://openalex.org/W2771683363","doi":"https://doi.org/10.1109/mfi.2017.8170390","title":"Information fusion of stock prices and sentiment in social media using Granger causality","display_name":"Information fusion of stock prices and sentiment in social media using Granger causality","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2771683363","doi":"https://doi.org/10.1109/mfi.2017.8170390","mag":"2771683363"},"language":"en","primary_location":{"id":"doi:10.1109/mfi.2017.8170390","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi.2017.8170390","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","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/A5052491114","display_name":"Jintak Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jintak Park","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Calgary, AB"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Calgary, AB","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061884304","display_name":"Henry Leung","orcid":"https://orcid.org/0000-0002-5984-107X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Henry Leung","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Calgary, AB"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Calgary, AB","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030553674","display_name":"King Ma","orcid":"https://orcid.org/0000-0002-6561-3217"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"King Ma","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Calgary, AB"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Calgary, AB","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5052491114"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5851,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.76656609,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"614","last_page":"619"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9991000294685364,"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":0.9991000294685364,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9958999752998352,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9939000010490417,"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/social-media","display_name":"Social media","score":0.7949981689453125},{"id":"https://openalex.org/keywords/granger-causality","display_name":"Granger causality","score":0.7915695905685425},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6704990863800049},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.6679675579071045},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.6041890978813171},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5599460005760193},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5297694206237793},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.5228050947189331},{"id":"https://openalex.org/keywords/lag","display_name":"Lag","score":0.44844964146614075},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.4228821098804474},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3019830584526062},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.2710413634777069},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11930397152900696},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.10973304510116577},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07402917742729187}],"concepts":[{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.7949981689453125},{"id":"https://openalex.org/C129824826","wikidata":"https://www.wikidata.org/wiki/Q2630107","display_name":"Granger causality","level":2,"score":0.7915695905685425},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6704990863800049},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.6679675579071045},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.6041890978813171},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5599460005760193},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5297694206237793},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5228050947189331},{"id":"https://openalex.org/C75778745","wikidata":"https://www.wikidata.org/wiki/Q342626","display_name":"Lag","level":2,"score":0.44844964146614075},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.4228821098804474},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3019830584526062},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.2710413634777069},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11930397152900696},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.10973304510116577},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07402917742729187},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mfi.2017.8170390","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi.2017.8170390","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W38739846","https://openalex.org/W133629798","https://openalex.org/W566109464","https://openalex.org/W647384071","https://openalex.org/W1814023381","https://openalex.org/W1851821411","https://openalex.org/W1918540025","https://openalex.org/W1967579779","https://openalex.org/W1983286042","https://openalex.org/W2002775973","https://openalex.org/W2043870592","https://openalex.org/W2101196063","https://openalex.org/W2108762681","https://openalex.org/W2111440106","https://openalex.org/W2133046612","https://openalex.org/W2140143008","https://openalex.org/W2171468534","https://openalex.org/W2178225550","https://openalex.org/W2220660951","https://openalex.org/W2516871718","https://openalex.org/W2588428500","https://openalex.org/W4211186029","https://openalex.org/W6601528862","https://openalex.org/W6621253433","https://openalex.org/W6638399519","https://openalex.org/W6685275399"],"related_works":["https://openalex.org/W2548633793","https://openalex.org/W3013279174","https://openalex.org/W2941935829","https://openalex.org/W2167436245","https://openalex.org/W126301054","https://openalex.org/W2596247554","https://openalex.org/W4301373556","https://openalex.org/W4251195004","https://openalex.org/W2133704721","https://openalex.org/W4323323727"],"abstract_inverted_index":{"Granger":[0],"causality":[1],"is":[2,19,50,82,103],"proposed":[3],"to":[4,71,74,88,105,110],"fuse":[5],"stock":[6,14,27,55,92],"prices":[7,93],"and":[8,54],"social":[9],"media":[10],"sentiment":[11,34,61,76],"information":[12],"for":[13,52],"market":[15],"prediction.":[16],"Sentiment":[17],"extraction":[18],"performed":[20,51],"on":[21],"the":[22,36,43,90],"Twitter":[23,53,67],"data":[24,56],"from":[25,57],"major":[26],"companies.":[28,59],"Analysis":[29],"shows":[30],"that":[31],"authoritative":[32],"user's":[33,72],"affects":[35],"other":[37],"users":[38],"after":[39],"an":[40],"event":[41],"with":[42,84,96],"lag":[44],"of":[45,66,78,94],"3":[46],"days.":[47],"The":[48,60,100],"prediction":[49,102],"four":[58],"analysis":[62],"algorithm":[63],"reflects":[64],"factors":[65],"which":[68],"are":[69],"relevant":[70],"authority":[73],"calculate":[75],"weight":[77],"each":[79],"message.":[80],"Information":[81],"fused":[83,101],"vector":[85],"autoregressive":[86],"models":[87],"predict":[89],"future":[91],"companies":[95],"large":[97],"twitter":[98],"support.":[99],"shown":[104],"have":[106],"improved":[107],"performance":[108],"compared":[109],"conventional":[111],"methods.":[112]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
