{"id":"https://openalex.org/W2471270217","doi":"https://doi.org/10.18653/v1/s16-1040","title":"ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter","display_name":"ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2471270217","doi":"https://doi.org/10.18653/v1/s16-1040","mag":"2471270217"},"language":"en","primary_location":{"id":"doi:10.18653/v1/s16-1040","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s16-1040","pdf_url":"https://www.aclweb.org/anthology/S16-1040.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 10th International Workshop on Semantic Evaluation (SemEval-2016)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/S16-1040.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018473115","display_name":"Yunxiao Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunxiao Zhou","raw_affiliation_strings":["Department of Computer Science and Technology, East China Normal University, Shanghai, P.R.China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, East China Normal University, Shanghai, P.R.China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100393616","display_name":"Zhihua Zhang","orcid":"https://orcid.org/0000-0003-3165-5213"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihua Zhang","raw_affiliation_strings":["Department of Computer Science and Technology, East China Normal University, Shanghai, P.R.China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, East China Normal University, Shanghai, P.R.China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057508424","display_name":"Man Lan","orcid":"https://orcid.org/0000-0002-1423-1286"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Man Lan","raw_affiliation_strings":["Department of Computer Science and Technology, East China Normal University, Shanghai, P.R.China","Shanghai Key Laboratory of Multidimensional Information Processing"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, East China Normal University, Shanghai, P.R.China","institution_ids":["https://openalex.org/I66867065"]},{"raw_affiliation_string":"Shanghai Key Laboratory of Multidimensional Information Processing","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5057508424"],"corresponding_institution_ids":["https://openalex.org/I66867065"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"256","last_page":"261"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9993000030517578,"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":0.9987000226974487,"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/semeval","display_name":"SemEval","score":0.9071873426437378},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8353139162063599},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.7762492895126343},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7661783695220947},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6189320683479309},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6080025434494019},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6013128161430359},{"id":"https://openalex.org/keywords/lexicon","display_name":"Lexicon","score":0.5760219693183899},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.5460118055343628},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.48834845423698425},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4810217022895813},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.47564029693603516},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4682423174381256},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.44252216815948486},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.17863452434539795},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07537814974784851}],"concepts":[{"id":"https://openalex.org/C44572571","wikidata":"https://www.wikidata.org/wiki/Q7448970","display_name":"SemEval","level":3,"score":0.9071873426437378},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8353139162063599},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7762492895126343},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7661783695220947},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6189320683479309},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6080025434494019},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6013128161430359},{"id":"https://openalex.org/C2778121359","wikidata":"https://www.wikidata.org/wiki/Q8096","display_name":"Lexicon","level":2,"score":0.5760219693183899},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.5460118055343628},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.48834845423698425},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4810217022895813},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.47564029693603516},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4682423174381256},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.44252216815948486},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.17863452434539795},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07537814974784851},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/s16-1040","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s16-1040","pdf_url":"https://www.aclweb.org/anthology/S16-1040.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 10th International Workshop on Semantic Evaluation (SemEval-2016)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/s16-1040","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s16-1040","pdf_url":"https://www.aclweb.org/anthology/S16-1040.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 10th International Workshop on Semantic Evaluation (SemEval-2016)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.8299999833106995,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G4760357681","display_name":null,"funder_award_id":"14DZ2260800","funder_id":"https://openalex.org/F4320321885","funder_display_name":"Science and Technology Commission of Shanghai Municipality"}],"funders":[{"id":"https://openalex.org/F4320321885","display_name":"Science and Technology Commission of Shanghai Municipality","ror":"https://ror.org/03kt66j61"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2471270217.pdf","grobid_xml":"https://content.openalex.org/works/W2471270217.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W371426616","https://openalex.org/W1509979276","https://openalex.org/W1614298861","https://openalex.org/W2108420397","https://openalex.org/W2108646579","https://openalex.org/W2142799022","https://openalex.org/W2158139315","https://openalex.org/W2170414372","https://openalex.org/W2250243742","https://openalex.org/W2250879510","https://openalex.org/W2335882425","https://openalex.org/W2460474657","https://openalex.org/W2752201871","https://openalex.org/W2998704965","https://openalex.org/W4285719527","https://openalex.org/W4385414156"],"related_works":["https://openalex.org/W2888662092","https://openalex.org/W3205826705","https://openalex.org/W2903394456","https://openalex.org/W2902285665","https://openalex.org/W3119550360","https://openalex.org/W2975174210","https://openalex.org/W2117643817","https://openalex.org/W4200238620","https://openalex.org/W2244029015","https://openalex.org/W2287843335"],"abstract_inverted_index":{"This":[0],"paper":[1],"reports":[2],"our":[3,102],"submissions":[4],"to":[5,45],"Task":[6],"4,":[7],"i.e.,":[8,29,39],"Sentiment":[9],"Analysis":[10],"in":[11,14,61],"Twitter":[12],"(SAT),":[13],"SemEval":[15],"2016,":[16],"which":[17],"consists":[18],"of":[19],"five":[20,64],"subtasks":[21],"grouped":[22],"into":[23],"two":[24],"levels:":[25],"(1)":[26],"sentence":[27],"level,":[28,38],"message":[30],"polarity":[31],"classification":[32,41],"(subtask":[33,48,55],"A),":[34],"and":[35,42,50,57,83,87],"(2)":[36],"topic":[37],"tweet":[40],"quantification":[43],"according":[44],"two-point":[46],"scale":[47,54],"B":[49],"D)":[51],"or":[52],"five-point":[53],"C":[56],"E).":[58],"We":[59],"participated":[60],"all":[62],"these":[63,68],"subtasks.":[65],"To":[66],"address":[67],"subtasks,":[69],"we":[70],"investigated":[71],"several":[72],"traditional":[73],"Natural":[74],"Language":[75],"Processing":[76],"(NLP)":[77],"features":[78,90],"including":[79],"sentiment":[80],"lexicon,":[81],"linguistic":[82],"domain":[84],"specific":[85],"features,":[86],"word":[88],"embedding":[89],"together":[91],"with":[92],"supervised":[93],"machine":[94],"learning":[95],"methods.":[96],"Officially":[97],"released":[98],"results":[99],"showed":[100],"that":[101],"systems":[103],"rank":[104],"above":[105],"average.":[106]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}