{"id":"https://openalex.org/W2773568398","doi":"https://doi.org/10.1109/smc.2017.8122712","title":"Predicting purchase intention according to fan page users' sentiment","display_name":"Predicting purchase intention according to fan page users' sentiment","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2773568398","doi":"https://doi.org/10.1109/smc.2017.8122712","mag":"2773568398"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2017.8122712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2017.8122712","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 Systems, Man, and Cybernetics (SMC)","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/A5031632498","display_name":"Li-Jen Kao","orcid":null},"institutions":[{"id":"https://openalex.org/I88780834","display_name":"Hwa Hsia University of Technology","ror":"https://ror.org/02mfjgm25","country_code":"TW","type":"education","lineage":["https://openalex.org/I88780834"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Li-Jen Kao","raw_affiliation_strings":["Department of Computer Science and Information Engineering, Hwa Hsia University of Technology, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, Hwa Hsia University of Technology, Taipei, Taiwan","institution_ids":["https://openalex.org/I88780834"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071391610","display_name":"Yo\u2010Ping Huang","orcid":"https://orcid.org/0000-0003-0429-2007"},"institutions":[{"id":"https://openalex.org/I118292597","display_name":"National Taipei University of Technology","ror":"https://ror.org/00cn92c09","country_code":"TW","type":"education","lineage":["https://openalex.org/I118292597"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yo-Ping Huang","raw_affiliation_strings":["Department of Electrical Engineering National Taipei University of Technology, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering National Taipei University of Technology, Taipei, Taiwan","institution_ids":["https://openalex.org/I118292597"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5031632498"],"corresponding_institution_ids":["https://openalex.org/I88780834"],"apc_list":null,"apc_paid":null,"fwci":2.8517,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.92862449,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"1","issue":null,"first_page":"831","last_page":"835"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10609","display_name":"Digital Marketing and Social Media","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10609","display_name":"Digital Marketing and Social Media","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social 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":0.9879000186920166,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9524999856948853,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.8041658401489258},{"id":"https://openalex.org/keywords/association-rule-learning","display_name":"Association rule learning","score":0.6721043586730957},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6381603479385376},{"id":"https://openalex.org/keywords/database-transaction","display_name":"Database transaction","score":0.5993708372116089},{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.5223339796066284},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.487468957901001},{"id":"https://openalex.org/keywords/harm","display_name":"Harm","score":0.47892817854881287},{"id":"https://openalex.org/keywords/advertising","display_name":"Advertising","score":0.4592750668525696},{"id":"https://openalex.org/keywords/transaction-data","display_name":"Transaction data","score":0.4425565004348755},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43773573637008667},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.34919285774230957},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.2903491258621216},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.28412503004074097},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1825423240661621},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1684376299381256},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.09691140055656433}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.8041658401489258},{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.6721043586730957},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6381603479385376},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.5993708372116089},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.5223339796066284},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.487468957901001},{"id":"https://openalex.org/C2777363581","wikidata":"https://www.wikidata.org/wiki/Q15098235","display_name":"Harm","level":2,"score":0.47892817854881287},{"id":"https://openalex.org/C112698675","wikidata":"https://www.wikidata.org/wiki/Q37038","display_name":"Advertising","level":1,"score":0.4592750668525696},{"id":"https://openalex.org/C127722929","wikidata":"https://www.wikidata.org/wiki/Q7833714","display_name":"Transaction data","level":3,"score":0.4425565004348755},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43773573637008667},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.34919285774230957},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.2903491258621216},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28412503004074097},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1825423240661621},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1684376299381256},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.09691140055656433},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc.2017.8122712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2017.8122712","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 Systems, Man, and Cybernetics (SMC)","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":21,"referenced_works":["https://openalex.org/W1495750374","https://openalex.org/W1971526329","https://openalex.org/W1991997945","https://openalex.org/W1997276421","https://openalex.org/W2039242902","https://openalex.org/W2042123098","https://openalex.org/W2043396212","https://openalex.org/W2056609785","https://openalex.org/W2068372750","https://openalex.org/W2077133447","https://openalex.org/W2088692072","https://openalex.org/W2098479684","https://openalex.org/W2108873424","https://openalex.org/W2142827986","https://openalex.org/W2149910108","https://openalex.org/W2151953639","https://openalex.org/W2186120071","https://openalex.org/W2339972044","https://openalex.org/W2469823140","https://openalex.org/W2743501893","https://openalex.org/W6720663644"],"related_works":["https://openalex.org/W2133788718","https://openalex.org/W3036124657","https://openalex.org/W2394010168","https://openalex.org/W2993700121","https://openalex.org/W2184109998","https://openalex.org/W2362772308","https://openalex.org/W2989589039","https://openalex.org/W3014936414","https://openalex.org/W2113309085","https://openalex.org/W2410549043"],"abstract_inverted_index":{"The":[0,31,46],"paper":[1],"proposes":[2],"a":[3,129],"data":[4],"mining":[5,139,194],"method":[6,195],"to":[7,22,42,57,70,119,142,156,169,196,219],"find":[8,102,143,197],"the":[9,29,55,59,74,88,95,103,112,147,160,165,177,185,198,203,222],"relationships":[10,104,204],"between":[11,105,205],"fan":[12,37,47,171,206],"page":[13,48,172,207],"users'":[14,98,106,173,208],"sentiment":[15,96,107,149,174,209],"and":[16,39,100,108,136,187,210],"customers'":[17,25,122,211],"purchase":[18,26,124,212],"behavior":[19],"in":[20,28,73],"order":[21],"predict":[23,120],"those":[24,65,83],"intention":[27],"future.":[30],"business":[32],"companies":[33],"create":[34],"their":[35,44,52,121,152],"own":[36],"pages":[38],"post":[40,51],"advertisements":[41],"prompt":[43],"products.":[45,62],"users":[49],"always":[50],"opinions":[53,66,84],"on":[54,132],"wall":[56],"tell":[58],"feelings":[60],"about":[61],"Since":[63],"all":[64],"will":[67],"be":[68,117,157],"spread":[69],"every":[71],"corner":[72],"social":[75],"network,":[76],"some":[77],"marketing":[78],"managers":[79],"would":[80],"wonder":[81],"whether":[82],"help":[85],"or":[86],"harm":[87],"products":[89,109,178],"sale.":[90],"If":[91],"we":[92,188],"can":[93,116,189],"measure":[94],"of":[97],"opinion":[99],"then":[101],"sales":[110,179],"volume,":[111],"discovered":[113],"sentiment-sales":[114,144,199],"patterns":[115,200],"used":[118,168],"future":[123],"intention.":[125],"In":[126],"this":[127],"study,":[128],"framework":[130,224],"based":[131],"fuzzy":[133,161],"set":[134],"model":[135],"association":[137,192],"rule":[138,193],"is":[140,182,217],"proposed":[141,223],"patterns.":[145],"First,":[146],"specific":[148],"topics":[150],"with":[151],"related":[153],"terms":[154,166],"need":[155],"defined.":[158],"Then":[159],"membership":[162],"functions":[163],"for":[164],"are":[167],"evaluate":[170],"score.":[175],"Assuming":[176],"volume":[180],"information":[181],"released":[183],"from":[184],"company,":[186],"use":[190],"inter-transaction":[191],"which":[201],"reveal":[202],"behavior.":[213],"A":[214],"theoretical":[215],"experiment":[216],"given":[218],"illustrate":[220],"how":[221],"works.":[225]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
