{"id":"https://openalex.org/W2083606554","doi":"https://doi.org/10.1109/bigdata.congress.2014.59","title":"A Study on Sentiment Computing and Classification of Sina Weibo with Word2vec","display_name":"A Study on Sentiment Computing and Classification of Sina Weibo with Word2vec","publication_year":2014,"publication_date":"2014-06-01","ids":{"openalex":"https://openalex.org/W2083606554","doi":"https://doi.org/10.1109/bigdata.congress.2014.59","mag":"2083606554"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.congress.2014.59","doi":"https://doi.org/10.1109/bigdata.congress.2014.59","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.congress.2014.59","pdf_url":null,"source":{"id":"https://openalex.org/S7407086525","display_name":"IEEE International Congress on Big Data (BigData Congress)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2014 IEEE International Congress on Big Data","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5100781916","display_name":"Xue Bai","orcid":"https://orcid.org/0000-0001-9717-846X","observed_orcids":["https://orcid.org/0000-0001-9717-846X"]},"institutions":[{"id":"https://openalex.org/I109173049","display_name":"Beijing Foreign Studies University","ror":"https://ror.org/00jdr0662","country_code":"CN","type":"education","lineage":["https://openalex.org/I109173049"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bai Xue","raw_affiliation_strings":["Department of Computer Science and Technology, Beijing Foreign Studies University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Beijing Foreign Studies University, Beijing, China","institution_ids":["https://openalex.org/I109173049"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015142571","display_name":"Chen Fu","orcid":"https://orcid.org/0000-0002-0397-7656","observed_orcids":["https://orcid.org/0000-0002-0397-7656","https://orcid.org/0009-0004-5640-9398"]},"institutions":[{"id":"https://openalex.org/I109173049","display_name":"Beijing Foreign Studies University","ror":"https://ror.org/00jdr0662","country_code":"CN","type":"education","lineage":["https://openalex.org/I109173049"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Fu","raw_affiliation_strings":["Department of Computer Science and Technology, Beijing Foreign Studies University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Beijing Foreign Studies University, Beijing, China","institution_ids":["https://openalex.org/I109173049"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100516601","display_name":"Zhan Shaobin","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I158809036","display_name":"Shenzhen Institute of Information Technology","ror":"https://ror.org/03wrf9427","country_code":"CN","type":"education","lineage":["https://openalex.org/I158809036"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhan Shaobin","raw_affiliation_strings":["Shenzhen institute of information & technology, Shenzhen, China","Shenzhen Inst. of Inf. & Technol., Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen institute of information & technology, Shenzhen, China","institution_ids":["https://openalex.org/I158809036"]},{"raw_affiliation_string":"Shenzhen Inst. of Inf. & Technol., Shenzhen, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.9722,"has_fulltext":false,"cited_by_count":137,"citation_normalized_percentile":{"value":0.98257232,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"358","last_page":"363"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":1.0,"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":1.0,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.0,"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":0.0,"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":0.9950000047683716},{"id":"https://openalex.org/keywords/weibo","display_name":"Weibo","score":0.8500000238418579},{"id":"https://openalex.org/keywords/microblogging","display_name":"microblogging","score":0.6029999852180481},{"id":"https://openalex.org/keywords/word2vec","display_name":"Word2Vec","score":0.9139999747276306},{"id":"https://openalex.org/keywords/sentiment-lexicon","display_name":"sentiment lexicon","score":0.6610000133514404},{"id":"https://openalex.org/keywords/semantic-orientation","display_name":"semantic orientation","score":0.6959999799728394},{"id":"https://openalex.org/keywords/chinese-text","display_name":"Chinese text","score":0.40400001406669617},{"id":"https://openalex.org/keywords/emotion-classification","display_name":"emotion classification","score":0.5509999990463257},{"id":"https://openalex.org/keywords/public-opinion-monitoring","display_name":"public opinion monitoring","score":0.6480000019073486}],"concepts":[{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.9198784828186035},{"id":"https://openalex.org/C143275388","wikidata":"https://www.wikidata.org/wiki/Q92438","display_name":"Microblogging","level":3,"score":0.8305038213729858},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7484327554702759},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7387992143630981},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.646436870098114},{"id":"https://openalex.org/C122980154","wikidata":"https://www.wikidata.org/wiki/Q205555","display_name":"Feeling","level":2,"score":0.5569900870323181},{"id":"https://openalex.org/C2988148770","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion detection","level":3,"score":0.4850308299064636},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.46493858098983765},{"id":"https://openalex.org/C7797323","wikidata":"https://www.wikidata.org/wiki/Q3798612","display_name":"Pointwise mutual information","level":3,"score":0.4394654333591461},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42968010902404785},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3518504798412323},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32922953367233276},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.3094393014907837},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.15516746044158936},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.15225493907928467},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.1111956536769867},{"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/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.congress.2014.59","doi":"https://doi.org/10.1109/bigdata.congress.2014.59","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.congress.2014.59","pdf_url":null,"source":{"id":"https://openalex.org/S7407086525","display_name":"IEEE International Congress on Big Data (BigData Congress)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2014 IEEE International Congress on Big Data","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1930023685","https://openalex.org/W2008803468","https://openalex.org/W2017445227","https://openalex.org/W2168625136","https://openalex.org/W2199803028","https://openalex.org/W2380107439","https://openalex.org/W2472761872","https://openalex.org/W4235131895"],"related_works":["https://openalex.org/W2982417585","https://openalex.org/W2912950904","https://openalex.org/W247794032","https://openalex.org/W3045639230","https://openalex.org/W2080467869","https://openalex.org/W2377954446","https://openalex.org/W2375421797","https://openalex.org/W3216293916","https://openalex.org/W88134586","https://openalex.org/W2036484797"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"Weibo":[3,104,130],"has":[4],"greatly":[5],"enriched":[6],"people's":[7],"life.":[8],"More":[9],"and":[10,19,23,41,66],"more":[11],"people":[12],"are":[13],"actively":[14],"sharing":[15],"information":[16,32],"with":[17],"others":[18],"expressing":[20],"their":[21],"opinions":[22],"feelings":[24],"on":[25,83],"Weibo.":[26],"Analyzing":[27],"emotion":[28],"hidden":[29],"in":[30,131],"this":[31,132],"can":[33],"benefit":[34],"online":[35],"marketing,":[36],"branding,":[37],"customer":[38],"relationship":[39],"management":[40],"monitoring":[42],"public":[43],"opinions.":[44],"Sentiment":[45,77],"analysis":[46,127],"is":[47,58],"to":[48,59,74,98],"identify":[49],"the":[50,54,95,100,107,111,125],"emotional":[51,101],"tendencies":[52,102],"of":[53,103,113,124,128],"microblog":[55],"messages,":[56],"that":[57],"classify":[60],"users'":[61],"emotions":[62],"into":[63],"positive,":[64],"negative":[65],"neutral.":[67],"This":[68],"paper":[69],"presents":[70],"a":[71,76,121],"novel":[72],"model":[73],"build":[75],"Dictionary":[78,97],"using":[79],"Word2vec":[80],"tool":[81],"based":[82],"our":[84,114],"Semantic":[85],"Orientation":[86],"Pointwise":[87],"Similarity":[88],"Distance":[89],"(SO-SD)":[90],"model.":[91],"Then":[92],"we":[93,109,118],"use":[94],"Emotional":[96],"obtain":[99],"messages.":[105],"Through":[106],"experiment,":[108],"validate":[110],"effectiveness":[112],"method,":[115],"by":[116],"which":[117],"have":[119],"performed":[120],"preliminary":[122],"exploration":[123],"sentiment":[126],"Chinese":[129],"paper.":[133]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":18},{"year":2019,"cited_by_count":21},{"year":2018,"cited_by_count":14},{"year":2017,"cited_by_count":18},{"year":2016,"cited_by_count":12},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2016-06-24T00:00:00"}
