{"id":"https://openalex.org/W286161961","doi":"https://doi.org/10.4018/ijirr.2014070105","title":"Tweet Sentiment Analysis with Latent Dirichlet Allocation","display_name":"Tweet Sentiment Analysis with Latent Dirichlet Allocation","publication_year":2014,"publication_date":"2014-07-01","ids":{"openalex":"https://openalex.org/W286161961","doi":"https://doi.org/10.4018/ijirr.2014070105","mag":"286161961"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijirr.2014070105","pdf_url":null,"source":{"id":"https://openalex.org/S4210223861","display_name":"International Journal of Information Retrieval Research","issn_l":"2155-6377","issn":["2155-6377","2155-6385"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-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/A5057551838","display_name":"Masahiro Ohmura","orcid":null},"institutions":[{"id":"https://openalex.org/I206011266","display_name":"Kwansei Gakuin University","ror":"https://ror.org/02qf2tx24","country_code":"JP","type":"education","lineage":["https://openalex.org/I206011266"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masahiro Ohmura","raw_affiliation_strings":["Kwansei Gakuin University, Sanda-shi, Japan"],"affiliations":[{"raw_affiliation_string":"Kwansei Gakuin University, Sanda-shi, Japan","institution_ids":["https://openalex.org/I206011266"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028631197","display_name":"Koh Kakusho","orcid":null},"institutions":[{"id":"https://openalex.org/I206011266","display_name":"Kwansei Gakuin University","ror":"https://ror.org/02qf2tx24","country_code":"JP","type":"education","lineage":["https://openalex.org/I206011266"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Koh Kakusho","raw_affiliation_strings":["Kwansei Gakuin University, Sanda-shi, Japan"],"affiliations":[{"raw_affiliation_string":"Kwansei Gakuin University, Sanda-shi, Japan","institution_ids":["https://openalex.org/I206011266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113946036","display_name":"Takeshi Okadome","orcid":null},"institutions":[{"id":"https://openalex.org/I206011266","display_name":"Kwansei Gakuin University","ror":"https://ror.org/02qf2tx24","country_code":"JP","type":"education","lineage":["https://openalex.org/I206011266"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takeshi Okadome","raw_affiliation_strings":["Kwansei Gakuin University, Sanda-shi, Japan"],"affiliations":[{"raw_affiliation_string":"Kwansei Gakuin University, Sanda-shi, Japan","institution_ids":["https://openalex.org/I206011266"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.991,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":5,"citation_normalized_percentile":{"value":0.728594,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":81,"max":82},"biblio":{"volume":"4","issue":"3","first_page":"66","last_page":"79"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9991,"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":0.9991,"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":0.9989,"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/T10028","display_name":"Topic Modeling","score":0.9972,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment Analysis","score":0.44641316}],"concepts":[{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.9431992},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.65765095},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.58712703},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.58531713},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.47036052},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4592746},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.44641316},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41064274},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.37122723},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.335303},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3297331},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2894288},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.05668792},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijirr.2014070105","pdf_url":null,"source":{"id":"https://openalex.org/S4210223861","display_name":"International Journal of Information Retrieval Research","issn_l":"2155-6377","issn":["2155-6377","2155-6385"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.69,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":13,"referenced_works":["https://openalex.org/W1590495275","https://openalex.org/W1743243001","https://openalex.org/W1880262756","https://openalex.org/W2008803468","https://openalex.org/W2015186536","https://openalex.org/W2018277822","https://openalex.org/W2021097538","https://openalex.org/W2042730970","https://openalex.org/W2171468534","https://openalex.org/W2174706414","https://openalex.org/W2267835966","https://openalex.org/W2473213829","https://openalex.org/W61081441"],"related_works":["https://openalex.org/W4315588616","https://openalex.org/W4312773271","https://openalex.org/W3159709618","https://openalex.org/W3005513013","https://openalex.org/W2962686197","https://openalex.org/W2888805565","https://openalex.org/W2769501189","https://openalex.org/W2611137333","https://openalex.org/W2207653751","https://openalex.org/W1982721348"],"abstract_inverted_index":{"The":[0,55],"method":[1,37],"proposed":[2],"here":[3],"analyzes":[4],"the":[5,36,83,91,121,129],"social":[6,45,76,103,124],"sentiments":[7,104,125],"from":[8],"collected":[9],"tweets":[10,25],"that":[11,68,105],"have":[12],"at":[13],"least":[14],"1":[15],"of":[16,48,90],"800":[17,92],"sentimental":[18],"or":[19,77],"emotional":[20],"adjectives.":[21],"By":[22],"dealing":[23],"with":[24,51,116],"posted":[26],"in":[27,64,111,119],"a":[28,30],"half":[29],"day":[31],"as":[32],"an":[33],"input":[34],"document,":[35],"uses":[38],"Latent":[39],"Dirichlet":[40],"Allocation":[41],"(LDA)":[42],"to":[43,62,87,101,109],"extract":[44],"sentiments,":[46,57],"some":[47],"which":[49,66,88,120],"coincide":[50],"our":[52],"daily":[53,75],"sentiments.":[54],"extracted":[56],"however,":[58],"indicate":[59],"lowered":[60],"sensitivity":[61,108],"changes":[63,110],"time,":[65],"suggests":[67],"they":[69],"are":[70,123],"not":[71],"suitable":[72],"for":[73,82],"predicting":[74],"economic":[78],"events.":[79],"Using":[80],"LDA":[81],"representative":[84],"72":[85],"adjectives":[86,93,131],"each":[89],"maps":[94],"while":[95],"preserving":[96],"word":[97],"frequencies":[98],"permits":[99],"us":[100],"obtain":[102],"show":[106],"improved":[107],"time.":[112],"A":[113],"regression":[114],"model":[115],"autocorrelated":[117],"errors":[118],"inputs":[122],"obtained":[126],"by":[127],"analyzing":[128],"contracted":[130],"predicts":[132],"Dow":[133],"Jones":[134],"Industrial":[135],"Average":[136],"(DJIA)":[137],"more":[138],"precisely":[139],"than":[140],"autoregressive":[141],"moving-average":[142],"models.":[143]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W286161961","counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2015,"cited_by_count":2}],"updated_date":"2024-12-24T08:58:21.363317","created_date":"2016-06-24"}