{"id":"https://openalex.org/W2752643126","doi":"https://doi.org/10.18653/v1/s17-2125","title":"funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts","display_name":"funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2752643126","doi":"https://doi.org/10.18653/v1/s17-2125","mag":"2752643126"},"language":"en","primary_location":{"id":"doi:10.18653/v1/s17-2125","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s17-2125","pdf_url":"https://www.aclweb.org/anthology/S17-2125.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 11th International Workshop on Semantic Evaluation\n          (SemEval-2017)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/S17-2125.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048436051","display_name":"Quanzhi Li","orcid":"https://orcid.org/0000-0002-4605-4237"},"institutions":[{"id":"https://openalex.org/I68384125","display_name":"Thomson Reuters (United States)","ror":"https://ror.org/00m7gt169","country_code":"US","type":"company","lineage":["https://openalex.org/I68384125"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Quanzhi Li","raw_affiliation_strings":["Research and Development Thomson Reuters 3 Times Square, NYC, NY 10036"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research and Development Thomson Reuters 3 Times Square, NYC, NY 10036","institution_ids":["https://openalex.org/I68384125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062396463","display_name":"Armineh Nourbakhsh","orcid":"https://orcid.org/0009-0004-1908-8679"},"institutions":[{"id":"https://openalex.org/I68384125","display_name":"Thomson Reuters (United States)","ror":"https://ror.org/00m7gt169","country_code":"US","type":"company","lineage":["https://openalex.org/I68384125"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Armineh Nourbakhsh","raw_affiliation_strings":["Research and Development Thomson Reuters 3 Times Square, NYC, NY 10036"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research and Development Thomson Reuters 3 Times Square, NYC, NY 10036","institution_ids":["https://openalex.org/I68384125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048424819","display_name":"Xiaomo Liu","orcid":"https://orcid.org/0000-0003-4184-4202"},"institutions":[{"id":"https://openalex.org/I68384125","display_name":"Thomson Reuters (United States)","ror":"https://ror.org/00m7gt169","country_code":"US","type":"company","lineage":["https://openalex.org/I68384125"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaomo Liu","raw_affiliation_strings":["Research and Development Thomson Reuters 3 Times Square, NYC, NY 10036"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research and Development Thomson Reuters 3 Times Square, NYC, NY 10036","institution_ids":["https://openalex.org/I68384125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102027188","display_name":"Rui Fang","orcid":"https://orcid.org/0009-0005-2597-9192"},"institutions":[{"id":"https://openalex.org/I68384125","display_name":"Thomson Reuters (United States)","ror":"https://ror.org/00m7gt169","country_code":"US","type":"company","lineage":["https://openalex.org/I68384125"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rui Fang","raw_affiliation_strings":["Research and Development Thomson Reuters 3 Times Square, NYC, NY 10036"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research and Development Thomson Reuters 3 Times Square, NYC, NY 10036","institution_ids":["https://openalex.org/I68384125"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103231131","display_name":"Sameena Shah","orcid":"https://orcid.org/0000-0002-8236-6465"},"institutions":[{"id":"https://openalex.org/I68384125","display_name":"Thomson Reuters (United States)","ror":"https://ror.org/00m7gt169","country_code":"US","type":"company","lineage":["https://openalex.org/I68384125"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sameena Shah","raw_affiliation_strings":["Research and Development Thomson Reuters 3 Times Square, NYC, NY 10036"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research and Development Thomson Reuters 3 Times Square, NYC, NY 10036","institution_ids":["https://openalex.org/I68384125"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I68384125"],"apc_list":null,"apc_paid":null,"fwci":0.2065,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64590723,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"741","last_page":"746"},"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/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":0.9979000091552734,"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":0.8471025228500366},{"id":"https://openalex.org/keywords/semeval","display_name":"SemEval","score":0.8228583335876465},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6691094040870667},{"id":"https://openalex.org/keywords/negation","display_name":"Negation","score":0.6545777916908264},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6472760438919067},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6442041397094727},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.6431045532226562},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6242735981941223},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.612740159034729},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.5757464170455933},{"id":"https://openalex.org/keywords/polarity","display_name":"Polarity (international relations)","score":0.5419924259185791},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47934088110923767},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.10404372215270996}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8471025228500366},{"id":"https://openalex.org/C44572571","wikidata":"https://www.wikidata.org/wiki/Q7448970","display_name":"SemEval","level":3,"score":0.8228583335876465},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6691094040870667},{"id":"https://openalex.org/C2185349","wikidata":"https://www.wikidata.org/wiki/Q190558","display_name":"Negation","level":2,"score":0.6545777916908264},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6472760438919067},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6442041397094727},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.6431045532226562},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6242735981941223},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.612740159034729},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.5757464170455933},{"id":"https://openalex.org/C2777361361","wikidata":"https://www.wikidata.org/wiki/Q1112585","display_name":"Polarity (international relations)","level":3,"score":0.5419924259185791},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47934088110923767},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.10404372215270996},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/s17-2125","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s17-2125","pdf_url":"https://www.aclweb.org/anthology/S17-2125.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 11th International Workshop on Semantic Evaluation\n          (SemEval-2017)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/s17-2125","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s17-2125","pdf_url":"https://www.aclweb.org/anthology/S17-2125.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 11th International Workshop on Semantic Evaluation\n          (SemEval-2017)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2752643126.pdf","grobid_xml":"https://content.openalex.org/works/W2752643126.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W40549020","https://openalex.org/W71795751","https://openalex.org/W142212369","https://openalex.org/W187383899","https://openalex.org/W193524605","https://openalex.org/W1512098439","https://openalex.org/W1703444797","https://openalex.org/W1987425720","https://openalex.org/W1996235486","https://openalex.org/W2031998113","https://openalex.org/W2108646579","https://openalex.org/W2118585731","https://openalex.org/W2125573226","https://openalex.org/W2143747826","https://openalex.org/W2151040995","https://openalex.org/W2153579005","https://openalex.org/W2154359981","https://openalex.org/W2158899491","https://openalex.org/W2166706824","https://openalex.org/W2250879510","https://openalex.org/W2251900677","https://openalex.org/W2251939518","https://openalex.org/W2529550020","https://openalex.org/W2626783607","https://openalex.org/W2916132663","https://openalex.org/W2949998441","https://openalex.org/W2951278869","https://openalex.org/W2952230511","https://openalex.org/W2963428430","https://openalex.org/W3146306708","https://openalex.org/W4294170691","https://openalex.org/W4382395375","https://openalex.org/W4385414156"],"related_works":["https://openalex.org/W2059922809","https://openalex.org/W2180954594","https://openalex.org/W2387527986","https://openalex.org/W2177124732","https://openalex.org/W4210537611","https://openalex.org/W2117643817","https://openalex.org/W2145128020","https://openalex.org/W2359533411","https://openalex.org/W2376091441","https://openalex.org/W1984947604"],"abstract_inverted_index":{"This":[0,98],"paper":[1],"describes":[2],"the":[3,42,67],"approach":[4],"we":[5,40],"used":[6,24],"for":[7,125],"SemEval-2017":[8],"Task":[9],"4:":[10],"Sentiment":[11],"Analysis":[12],"in":[13,25,64,95,117,141,153,163,168],"Twitter.":[14],"Topic-based":[15],"(target-dependent)":[16],"sentiment":[17],"analysis":[18],"has":[19],"become":[20],"attractive":[21],"and":[22,44,76,109,148,159,166],"been":[23],"some":[26],"applications":[27],"recently,":[28],"but":[29],"it":[30],"is":[31,138],"still":[32],"a":[33,48,91,110],"challenging":[34],"research":[35],"task.":[36],"In":[37],"our":[38,65,96],"approach,":[39],"take":[41],"left":[43],"right":[45],"context":[46],"of":[47,61,128],"target":[49],"into":[50],"consideration":[51],"when":[52],"generating":[53],"polarity":[54,134],"classification":[55,113],"features.":[56],"We":[57,88,115],"use":[58],"two":[59],"types":[60],"word":[62,69,78],"embeddings":[63,70,79],"classifiers:":[66],"general":[68],"learned":[71,80],"from":[72,81],"200":[73],"million":[74,83],"tweets,":[75],"sentiment-specific":[77],"10":[82],"tweets":[84],"using":[85,157,161],"distance":[86],"supervision.":[87],"also":[89],"incorporate":[90],"text":[92,104,112],"feature":[93],"model":[94,99],"algorithm.":[97],"produces":[100],"features":[101],"based":[102],"on":[103],"negation,":[105],"tf.idf":[106],"weighting":[107],"scheme,":[108],"Rocchio":[111],"method.":[114],"participated":[116],"four":[118],"subtasks":[119],"(B,":[120],"C,":[121,155],"D":[122],"&":[123],"E":[124],"English),":[126],"all":[127],"which":[129],"are":[130],"about":[131],"topic-based":[132],"message":[133],"classification.":[135],"Our":[136],"team":[137],"ranked":[139],"#6":[140,160],"subtask":[142,154,164,169],"B,":[143],"#3":[144,156,167],"by":[145,150],"MAE":[146,151],"u":[147],"#9":[149],"m":[152],"RAE":[158],"KLD":[162],"D,":[165],"E.":[170]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}