{"id":"https://openalex.org/W2751358090","doi":"https://doi.org/10.18653/v1/s17-2149","title":"UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation","display_name":"UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2751358090","doi":"https://doi.org/10.18653/v1/s17-2149","mag":"2751358090"},"language":"en","primary_location":{"id":"doi:10.18653/v1/s17-2149","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s17-2149","pdf_url":"https://www.aclweb.org/anthology/S17-2149.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/S17-2149.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019551431","display_name":"Vineet John","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Vineet John","raw_affiliation_strings":["University of Waterloo"],"affiliations":[{"raw_affiliation_string":"University of Waterloo","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028031104","display_name":"Olga Vechtomova","orcid":"https://orcid.org/0000-0001-7371-0837"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Olga Vechtomova","raw_affiliation_strings":["University of Waterloo"],"affiliations":[{"raw_affiliation_string":"University of Waterloo","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5019551431"],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":0.195,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.62046076,"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":"872","last_page":"876"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9990000128746033,"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/bigram","display_name":"Bigram","score":0.88351970911026},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8564399480819702},{"id":"https://openalex.org/keywords/semeval","display_name":"SemEval","score":0.8046746850013733},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7122339606285095},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6472418308258057},{"id":"https://openalex.org/keywords/vectorization","display_name":"Vectorization (mathematics)","score":0.6255697011947632},{"id":"https://openalex.org/keywords/paragraph","display_name":"Paragraph","score":0.6155307292938232},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6110213994979858},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5280094146728516},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4894038736820221},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4693053066730499},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.44620200991630554},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41970810294151306},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.4129628837108612},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.12601444125175476},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09545981884002686},{"id":"https://openalex.org/keywords/trigram","display_name":"Trigram","score":0.09085997939109802}],"concepts":[{"id":"https://openalex.org/C108757681","wikidata":"https://www.wikidata.org/wiki/Q2773912","display_name":"Bigram","level":3,"score":0.88351970911026},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8564399480819702},{"id":"https://openalex.org/C44572571","wikidata":"https://www.wikidata.org/wiki/Q7448970","display_name":"SemEval","level":3,"score":0.8046746850013733},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7122339606285095},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6472418308258057},{"id":"https://openalex.org/C41681595","wikidata":"https://www.wikidata.org/wiki/Q7917855","display_name":"Vectorization (mathematics)","level":2,"score":0.6255697011947632},{"id":"https://openalex.org/C2777206241","wikidata":"https://www.wikidata.org/wiki/Q194431","display_name":"Paragraph","level":2,"score":0.6155307292938232},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6110213994979858},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5280094146728516},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4894038736820221},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4693053066730499},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.44620200991630554},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41970810294151306},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4129628837108612},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.12601444125175476},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09545981884002686},{"id":"https://openalex.org/C137546455","wikidata":"https://www.wikidata.org/wiki/Q3213474","display_name":"Trigram","level":2,"score":0.09085997939109802},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/s17-2149","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s17-2149","pdf_url":"https://www.aclweb.org/anthology/S17-2149.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/s17-2149","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s17-2149","pdf_url":"https://www.aclweb.org/anthology/S17-2149.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/W2751358090.pdf","grobid_xml":"https://content.openalex.org/works/W2751358090.grobid-xml"},"referenced_works_count":11,"referenced_works":["https://openalex.org/W1546425147","https://openalex.org/W2070076962","https://openalex.org/W2101234009","https://openalex.org/W2131744502","https://openalex.org/W2154359981","https://openalex.org/W2291445757","https://openalex.org/W2295598076","https://openalex.org/W2493916176","https://openalex.org/W2753259282","https://openalex.org/W2963626623","https://openalex.org/W3102476541"],"related_works":["https://openalex.org/W3173084154","https://openalex.org/W2334448532","https://openalex.org/W2982021180","https://openalex.org/W2131563376","https://openalex.org/W2251497876","https://openalex.org/W2241081188","https://openalex.org/W159278796","https://openalex.org/W2128567707","https://openalex.org/W4327499987","https://openalex.org/W2011383762"],"abstract_inverted_index":{"This":[0,108],"paper":[1,29,97],"discusses":[2],"the":[3,7,16,31,43,51,76,80,92,105],"approach":[4],"taken":[5,48],"by":[6,22],"UWaterloo":[8],"team":[9],"to":[10,74,103],"arrive":[11],"at":[12],"a":[13],"solution":[14],"for":[15,53,112],"Fine-Grained":[17],"Sentiment":[18],"Analysis":[19],"problem":[20],"posed":[21],"Task":[23],"5":[24],"of":[25],"SemEval":[26],"2017.":[27],"The":[28,56,96],"describes":[30],"document":[32],"vectorization":[33,60],"and":[34,45,66,84,117],"sentiment":[35,77],"score":[36],"prediction":[37],"techniques":[38],"used,":[39],"as":[40,42,63],"well":[41],"design":[44],"implementation":[46],"decisions":[47],"while":[49],"building":[50],"system":[52,57,109],"this":[54],"task.":[55],"uses":[58],"text":[59],"models,":[61],"such":[62],"N-gram,":[64],"TF-IDF":[65],"paragraph":[67],"embeddings,":[68],"coupled":[69,86],"with":[70,87],"regression":[71,90],"model":[72],"variants":[73],"predict":[75],"scores.":[78],"Amongst":[79],"methods":[81,102],"examined,":[82],"unigrams":[83],"bigrams":[85],"simple":[88],"linear":[89],"obtained":[91],"best":[93],"baseline":[94],"accuracy.":[95],"also":[98],"explores":[99],"data":[100],"augmentation":[101],"supplement":[104],"training":[106],"dataset.":[107],"was":[110],"designed":[111],"Subtask":[113],"2":[114],"(News":[115],"Statements":[116],"Headlines).":[118]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
