{"id":"https://openalex.org/W2296229202","doi":"https://doi.org/10.1109/bigcomp.2016.7425916","title":"Weighted multi-label classification model for sentiment analysis of online news","display_name":"Weighted multi-label classification model for sentiment analysis of online news","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2296229202","doi":"https://doi.org/10.1109/bigcomp.2016.7425916","mag":"2296229202"},"language":"en","primary_location":{"id":"doi:10.1109/bigcomp.2016.7425916","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigcomp.2016.7425916","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Big Data and Smart Computing (BigComp)","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/A5100353780","display_name":"Xin Li","orcid":"https://orcid.org/0000-0002-0041-3134"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xin Li","raw_affiliation_strings":["Sun Yatsen University, Guang Dong, China"],"affiliations":[{"raw_affiliation_string":"Sun Yatsen University, Guang Dong, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013151488","display_name":"Haoran Xie","orcid":"https://orcid.org/0000-0003-0965-3617"},"institutions":[{"id":"https://openalex.org/I1877545","display_name":"Saint Francis University","ror":"https://ror.org/01wcz2f33","country_code":"HK","type":"education","lineage":["https://openalex.org/I1877545"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Haoran Xie","raw_affiliation_strings":["Caritas Institute of Higher Education, Hong Kong, SAR, China"],"affiliations":[{"raw_affiliation_string":"Caritas Institute of Higher Education, Hong Kong, SAR, China","institution_ids":["https://openalex.org/I1877545"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058291454","display_name":"Yanghui Rao","orcid":"https://orcid.org/0000-0003-1610-9599"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanghui Rao","raw_affiliation_strings":["Sun Yatsen University, Guang Dong, China"],"affiliations":[{"raw_affiliation_string":"Sun Yatsen University, Guang Dong, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033484646","display_name":"Yanjia Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanjia Chen","raw_affiliation_strings":["Sun Yatsen University, Guang Dong, China"],"affiliations":[{"raw_affiliation_string":"Sun Yatsen University, Guang Dong, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101599729","display_name":"Xuebo Liu","orcid":"https://orcid.org/0000-0001-8524-2006"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuebo Liu","raw_affiliation_strings":["Sun Yatsen University, Guang Dong, China"],"affiliations":[{"raw_affiliation_string":"Sun Yatsen University, Guang Dong, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025290614","display_name":"Huan Huang","orcid":"https://orcid.org/0000-0002-4429-9476"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huan Huang","raw_affiliation_strings":["Sun Yatsen University, Guang Dong, China"],"affiliations":[{"raw_affiliation_string":"Sun Yatsen University, Guang Dong, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072482402","display_name":"Fu Lee Wang","orcid":"https://orcid.org/0000-0002-3976-0053"},"institutions":[{"id":"https://openalex.org/I1877545","display_name":"Saint Francis University","ror":"https://ror.org/01wcz2f33","country_code":"HK","type":"education","lineage":["https://openalex.org/I1877545"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Fu Lee Wang","raw_affiliation_strings":["Caritas Institute of Higher Education, Hong Kong, SAR, China"],"affiliations":[{"raw_affiliation_string":"Caritas Institute of Higher Education, Hong Kong, SAR, China","institution_ids":["https://openalex.org/I1877545"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5100353780"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":5.1417,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.95607773,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"215","last_page":"222"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9994000196456909,"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":0.9994000196456909,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9994000196456909,"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":0.9980999827384949,"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/computer-science","display_name":"Computer science","score":0.7387708425521851},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6987646222114563},{"id":"https://openalex.org/keywords/multi-label-classification","display_name":"Multi-label classification","score":0.5383371710777283},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5258467793464661},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.36917272210121155},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3538685441017151},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.343327134847641}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7387708425521851},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6987646222114563},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.5383371710777283},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5258467793464661},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.36917272210121155},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3538685441017151},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.343327134847641}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigcomp.2016.7425916","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigcomp.2016.7425916","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Big Data and Smart Computing (BigComp)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W72424516","https://openalex.org/W170592701","https://openalex.org/W193333623","https://openalex.org/W648130861","https://openalex.org/W1532325895","https://openalex.org/W1565377632","https://openalex.org/W1612003148","https://openalex.org/W1744270772","https://openalex.org/W1880262756","https://openalex.org/W1965235124","https://openalex.org/W1967542092","https://openalex.org/W1970381522","https://openalex.org/W1989792746","https://openalex.org/W1993816389","https://openalex.org/W1998839399","https://openalex.org/W1999320905","https://openalex.org/W1999954155","https://openalex.org/W2001082470","https://openalex.org/W2018222191","https://openalex.org/W2029517229","https://openalex.org/W2045599215","https://openalex.org/W2045631398","https://openalex.org/W2048101063","https://openalex.org/W2048195127","https://openalex.org/W2048658075","https://openalex.org/W2052684427","https://openalex.org/W2072007696","https://openalex.org/W2084465406","https://openalex.org/W2104090402","https://openalex.org/W2105468141","https://openalex.org/W2107474859","https://openalex.org/W2108420397","https://openalex.org/W2108646579","https://openalex.org/W2110162085","https://openalex.org/W2114315281","https://openalex.org/W2126854223","https://openalex.org/W2129144539","https://openalex.org/W2143570397","https://openalex.org/W2161824996","https://openalex.org/W2164972036","https://openalex.org/W2166706824","https://openalex.org/W2250511935","https://openalex.org/W2395434972","https://openalex.org/W2482589566","https://openalex.org/W2573073746","https://openalex.org/W2612769033","https://openalex.org/W4213009331","https://openalex.org/W4231510805","https://openalex.org/W4239510810","https://openalex.org/W6602936745","https://openalex.org/W6606905564","https://openalex.org/W6636440780","https://openalex.org/W6639619044","https://openalex.org/W6731568869"],"related_works":["https://openalex.org/W2548633793","https://openalex.org/W3013279174","https://openalex.org/W2941935829","https://openalex.org/W2596247554","https://openalex.org/W3132372214","https://openalex.org/W4224284088","https://openalex.org/W4286571989","https://openalex.org/W2765903680","https://openalex.org/W4317653575","https://openalex.org/W2801635251"],"abstract_inverted_index":{"With":[0],"the":[1,23,35,40,67,74,83,103,107,120,123],"extensive":[2],"growth":[3],"of":[4,42,69,76,85,102,122],"social":[5],"media":[6],"services,":[7],"many":[8],"users":[9],"express":[10],"their":[11],"feelings":[12],"and":[13,19,116],"opinions":[14],"through":[15],"news":[16,114],"articles,":[17],"blogs":[18],"tweets/microblogs.":[20],"To":[21],"discover":[22],"connections":[24],"between":[25],"emotions":[26],"evoked":[27],"in":[28,79],"a":[29,57],"user":[30],"by":[31,65],"varied-scale":[32],"documents":[33,55,118],"effectively,":[34],"paper":[36],"is":[37,63,94],"concerned":[38],"with":[39],"problem":[41],"sentiment":[43,127],"analysis":[44],"over":[45],"online":[46],"news.":[47],"Different":[48],"from":[49],"previous":[50],"models":[51],"which":[52],"treat":[53],"training":[54,77],"uniformly,":[56],"weighted":[58],"multi-label":[59],"classification":[60],"model":[61],"(WMCM)":[62],"proposed":[64,124],"introducing":[66],"concept":[68],"\u201cemotional":[70],"concentration\u201d":[71],"to":[72,81,97],"estimate":[73],"weight":[75],"documents,":[78],"addition":[80],"tackle":[82],"issue":[84],"noisy":[86],"samples":[87],"for":[88,126],"each":[89],"emotion.":[90],"The":[91],"topic":[92],"assignment":[93],"also":[95],"used":[96],"distinguish":[98],"different":[99],"emotional":[100],"senses":[101],"same":[104],"word":[105],"at":[106],"semantic":[108],"level.":[109],"Experimental":[110],"evaluations":[111],"using":[112],"short":[113],"headlines":[115],"long":[117],"validate":[119],"effectiveness":[121],"WMCM":[125],"prediction.":[128]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}