{"id":"https://openalex.org/W2126743576","doi":"https://doi.org/10.18653/v1/s15-2086","title":"Splusplus: A Feature-Rich Two-stage Classifier for Sentiment Analysis of Tweets","display_name":"Splusplus: A Feature-Rich Two-stage Classifier for Sentiment Analysis of Tweets","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2126743576","doi":"https://doi.org/10.18653/v1/s15-2086","mag":"2126743576"},"language":"en","primary_location":{"id":"doi:10.18653/v1/s15-2086","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s15-2086","pdf_url":"https://aclanthology.org/S15-2086.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/S15-2086.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103661051","display_name":"Li Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Li Dong","raw_affiliation_strings":["Beihang University, Beijing, 100191, China","Beihang Univ.#TAB#"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, 100191, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"Beihang Univ.#TAB#","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014662947","display_name":"Furu Wei","orcid":"https://orcid.org/0000-0002-7810-5852"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]},{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN","GB"],"is_corresponding":false,"raw_author_name":"Furu Wei","raw_affiliation_strings":["Microsoft Research, Beijing, 100080, China","Microsoft"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, 100080, China","institution_ids":["https://openalex.org/I4210113369"]},{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043780805","display_name":"Yichun Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]},{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN","GB"],"is_corresponding":false,"raw_author_name":"Yichun Yin","raw_affiliation_strings":["Peking University, Beijing, 100871, China","Microsoft"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100701572","display_name":"Ming Zhou","orcid":"https://orcid.org/0000-0002-2551-2964"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]},{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Zhou","raw_affiliation_strings":["Microsoft Research, Beijing, 100080, China","Peking University"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, 100080, China","institution_ids":["https://openalex.org/I4210113369"]},{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107234003","display_name":"Ke Xu","orcid":"https://orcid.org/0000-0001-5447-8008"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Xu","raw_affiliation_strings":["Beihang University, Beijing, 100191, China","Beihang Univ.#TAB#"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, 100191, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"Beihang Univ.#TAB#","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5103661051"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":2.6707,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.91778384,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"515","last_page":"519"},"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/T10028","display_name":"Topic Modeling","score":0.9988999962806702,"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":0.9983999729156494,"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/computer-science","display_name":"Computer science","score":0.831339955329895},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7385600805282593},{"id":"https://openalex.org/keywords/semeval","display_name":"SemEval","score":0.7087780237197876},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7055037617683411},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.7002952694892883},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6886236667633057},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.45086461305618286},{"id":"https://openalex.org/keywords/polarity","display_name":"Polarity (international relations)","score":0.4283398985862732},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.41443705558776855},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3528280556201935},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3514173626899719}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.831339955329895},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7385600805282593},{"id":"https://openalex.org/C44572571","wikidata":"https://www.wikidata.org/wiki/Q7448970","display_name":"SemEval","level":3,"score":0.7087780237197876},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7055037617683411},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7002952694892883},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6886236667633057},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.45086461305618286},{"id":"https://openalex.org/C2777361361","wikidata":"https://www.wikidata.org/wiki/Q1112585","display_name":"Polarity (international relations)","level":3,"score":0.4283398985862732},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41443705558776855},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3528280556201935},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3514173626899719},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C1491633281","wikidata":"https://www.wikidata.org/wiki/Q7868","display_name":"Cell","level":2,"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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/s15-2086","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s15-2086","pdf_url":"https://aclanthology.org/S15-2086.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/s15-2086","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s15-2086","pdf_url":"https://aclanthology.org/S15-2086.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G3085993365","display_name":null,"funder_award_id":"(Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3935756157","display_name":null,"funder_award_id":"142100","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5249178904","display_name":null,"funder_award_id":"Grant No. 6","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7726157001","display_name":null,"funder_award_id":"Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8055127673","display_name":null,"funder_award_id":"61421003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2126743576.pdf","grobid_xml":"https://content.openalex.org/works/W2126743576.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W233613663","https://openalex.org/W371426616","https://openalex.org/W1498436455","https://openalex.org/W1832693441","https://openalex.org/W2022204871","https://openalex.org/W2097726431","https://openalex.org/W2118585731","https://openalex.org/W2153579005","https://openalex.org/W2156413587","https://openalex.org/W2158899491","https://openalex.org/W2160660844","https://openalex.org/W2250243742","https://openalex.org/W2250879510","https://openalex.org/W2251303898","https://openalex.org/W2251939518","https://openalex.org/W2460474657","https://openalex.org/W2467186984","https://openalex.org/W2952230511","https://openalex.org/W4235765578","https://openalex.org/W4294170691"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W3029012650","https://openalex.org/W1540371141","https://openalex.org/W4231274751","https://openalex.org/W1988325893","https://openalex.org/W2776212826","https://openalex.org/W2117643817","https://openalex.org/W1984947604","https://openalex.org/W2798693377","https://openalex.org/W4385570771"],"abstract_inverted_index":{"This":[0],"paper":[1],"describes":[2],"our":[3,95],"sentiment":[4,40,80],"classification":[5,16,53,81],"system":[6],"submitted":[7],"to":[8,37,47,59,73],"SemEval-2015":[9],"Task":[10],"10.":[11],"In":[12,57],"the":[13,20,39,78,92],"message-level":[14,79],"polarity":[15,87],"subtask,":[17],"we":[18,32,67,84],"obtain":[19],"highest":[21],"macroaveraged":[22],"F1-scores":[23],"on":[24],"three":[25],"out":[26],"of":[27,94],"six":[28],"testing":[29],"sets.":[30],"Specifically,":[31],"build":[33],"a":[34,86],"two-stage":[35],"classifier":[36],"predict":[38],"labels":[41],"for":[42,51,77],"tweets,":[43],"which":[44,90],"enables":[45],"us":[46],"design":[48],"different":[49],"features":[50,76],"subjective/objective":[52],"and":[54,64],"positive/negative":[55],"classification.":[56],"addition":[58],"n-grams,":[60],"lexicons,":[61],"word":[62],"clusters,":[63],"twitter-specific":[65],"features,":[66],"develop":[68],"several":[69],"deep":[70],"learning":[71],"methods":[72],"automatically":[74],"extract":[75],"task.":[82],"Moreover,":[83],"propose":[85],"boosting":[88],"trick":[89],"improves":[91],"performance":[93],"system.":[96]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":5}],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}
