{"id":"https://openalex.org/W2597358511","doi":"https://doi.org/10.18653/v1/e17-2090","title":"Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering","display_name":"Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2597358511","doi":"https://doi.org/10.18653/v1/e17-2090","mag":"2597358511"},"language":"en","primary_location":{"id":"doi:10.18653/v1/e17-2090","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/e17-2090","pdf_url":"https://www.aclweb.org/anthology/E17-2090.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 15th Conference of the European Chapter of the\n          Association for Computational Linguistics: Volume 2, Short Papers","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/E17-2090.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058954591","display_name":"Jo\u00e3o Sedoc","orcid":"https://orcid.org/0000-0001-6369-3711"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Joao Sedoc","raw_affiliation_strings":["Positive Psychology Center Computer & Information Science University of Pennsylvania"],"affiliations":[{"raw_affiliation_string":"Positive Psychology Center Computer & Information Science University of Pennsylvania","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086173474","display_name":"Daniel Preo\u021biuc-Pietro","orcid":"https://orcid.org/0000-0002-4504-0212"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Preo\u0163iuc-Pietro","raw_affiliation_strings":["Positive Psychology Center Computer & Information Science University of Pennsylvania"],"affiliations":[{"raw_affiliation_string":"Positive Psychology Center Computer & Information Science University of Pennsylvania","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044944954","display_name":"Lyle Ungar","orcid":"https://orcid.org/0000-0003-2047-1443"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lyle Ungar","raw_affiliation_strings":["Positive Psychology Center Computer & Information Science University of Pennsylvania"],"affiliations":[{"raw_affiliation_string":"Positive Psychology Center Computer & Information Science University of Pennsylvania","institution_ids":["https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5058954591"],"corresponding_institution_ids":["https://openalex.org/I79576946"],"apc_list":null,"apc_paid":null,"fwci":2.7022,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":{"value":0.92186442,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"564","last_page":"571"},"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":0.9987999796867371,"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":0.9986000061035156,"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":0.7276051044464111},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.707960844039917},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.6657860279083252},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6171246767044067},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5978667736053467},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5688738226890564},{"id":"https://openalex.org/keywords/valence","display_name":"Valence (chemistry)","score":0.5403710603713989},{"id":"https://openalex.org/keywords/psycholinguistics","display_name":"Psycholinguistics","score":0.5019965171813965},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.4875630736351013},{"id":"https://openalex.org/keywords/affect","display_name":"Affect (linguistics)","score":0.4352530241012573},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3996240198612213},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.21057897806167603},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.1945902705192566},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.1926213800907135},{"id":"https://openalex.org/keywords/communication","display_name":"Communication","score":0.08426675200462341}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7276051044464111},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.707960844039917},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.6657860279083252},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6171246767044067},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5978667736053467},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5688738226890564},{"id":"https://openalex.org/C168900304","wikidata":"https://www.wikidata.org/wiki/Q171407","display_name":"Valence (chemistry)","level":2,"score":0.5403710603713989},{"id":"https://openalex.org/C89267518","wikidata":"https://www.wikidata.org/wiki/Q179488","display_name":"Psycholinguistics","level":3,"score":0.5019965171813965},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.4875630736351013},{"id":"https://openalex.org/C2776035688","wikidata":"https://www.wikidata.org/wiki/Q1606558","display_name":"Affect (linguistics)","level":2,"score":0.4352530241012573},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3996240198612213},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.21057897806167603},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1945902705192566},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.1926213800907135},{"id":"https://openalex.org/C46312422","wikidata":"https://www.wikidata.org/wiki/Q11024","display_name":"Communication","level":1,"score":0.08426675200462341},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/e17-2090","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/e17-2090","pdf_url":"https://www.aclweb.org/anthology/E17-2090.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 15th Conference of the European Chapter of the\n          Association for Computational Linguistics: Volume 2, Short Papers","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/e17-2090","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/e17-2090","pdf_url":"https://www.aclweb.org/anthology/E17-2090.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 15th Conference of the European Chapter of the\n          Association for Computational Linguistics: Volume 2, Short Papers","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.8299999833106995}],"awards":[{"id":"https://openalex.org/G8894517126","display_name":null,"funder_award_id":"TRT-0048","funder_id":"https://openalex.org/F4320327997","funder_display_name":"Templeton Religion Trust"}],"funders":[{"id":"https://openalex.org/F4320327997","display_name":"Templeton Religion Trust","ror":"https://ror.org/02q53mk25"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2597358511.pdf","grobid_xml":"https://content.openalex.org/works/W2597358511.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W1241017059","https://openalex.org/W1486777056","https://openalex.org/W1503259811","https://openalex.org/W1580265784","https://openalex.org/W1974991592","https://openalex.org/W1979532929","https://openalex.org/W1999609000","https://openalex.org/W2023736093","https://openalex.org/W2040467972","https://openalex.org/W2043632943","https://openalex.org/W2048795844","https://openalex.org/W2069997377","https://openalex.org/W2091880272","https://openalex.org/W2097162496","https://openalex.org/W2097726431","https://openalex.org/W2101251342","https://openalex.org/W2121947440","https://openalex.org/W2124217660","https://openalex.org/W2132914434","https://openalex.org/W2135674549","https://openalex.org/W2141599568","https://openalex.org/W2149628368","https://openalex.org/W2150159277","https://openalex.org/W2165857685","https://openalex.org/W2168625136","https://openalex.org/W2211714781","https://openalex.org/W2250539671","https://openalex.org/W2250879510","https://openalex.org/W2251592867","https://openalex.org/W2251939208","https://openalex.org/W2251979305","https://openalex.org/W2290759784","https://openalex.org/W2296273013","https://openalex.org/W2339570520","https://openalex.org/W2397535132","https://openalex.org/W2403710930","https://openalex.org/W2496889969","https://openalex.org/W2577060182","https://openalex.org/W2882319491","https://openalex.org/W2949998441","https://openalex.org/W2951319051","https://openalex.org/W2962914241","https://openalex.org/W2963302148","https://openalex.org/W2964232431","https://openalex.org/W2964325543","https://openalex.org/W2979401726","https://openalex.org/W3146306708","https://openalex.org/W4285719527","https://openalex.org/W4293763439","https://openalex.org/W4297575547","https://openalex.org/W4365799807"],"related_works":["https://openalex.org/W3118224419","https://openalex.org/W2040515868","https://openalex.org/W4313204384","https://openalex.org/W2369801737","https://openalex.org/W2801147070","https://openalex.org/W1563707621","https://openalex.org/W2140849905","https://openalex.org/W2392026738","https://openalex.org/W2379594506","https://openalex.org/W2144538087"],"abstract_inverted_index":{"Inferring":[0],"the":[1,70,75],"emotional":[2],"content":[3],"of":[4,45,73,80],"words":[5,41],"is":[6],"important":[7],"for":[8,113],"text-based":[9],"sentiment":[10,122],"analysis,":[11],"dialogue":[12],"systems":[13],"and":[14,25,63,116,124],"psycholinguistics,":[15],"but":[16],"word":[17,48,87,102,111],"ratings":[18,38,112],"are":[19],"expensive":[20],"to":[21,39,58,100],"collect":[22],"at":[23],"scale":[24],"across":[26,94],"languages":[27,97,115],"or":[28],"domains.":[29],"We":[30,54],"develop":[31],"a":[32,60],"method":[33,57,83,107],"that":[34],"automatically":[35],"extends":[36],"word-level":[37],"unrated":[40],"using":[42],"signed":[43],"clustering":[44],"vector":[46],"space":[47],"representations":[49],"along":[50],"with":[51],"affect":[52],"ratings.":[53],"use":[55],"our":[56],"determine":[59,66],"word's":[61],"valence":[62],"arousal,":[64],"which":[65],"its":[67],"position":[68],"on":[69,90],"circumplex":[71],"model":[72,79],"affect,":[74],"most":[76],"popular":[77],"dimensional":[78],"emotion.":[81],"Our":[82,106],"achieves":[84],"superior":[85],"out-of-sample":[86],"rating":[88],"prediction":[89],"both":[91],"affective":[92],"dimensions":[93],"three":[95],"different":[96],"when":[98],"compared":[99],"state-of-theart":[101],"similarity":[103],"based":[104],"methods.":[105],"can":[108],"assist":[109],"building":[110],"new":[114],"improve":[117],"downstream":[118],"tasks":[119],"such":[120],"as":[121],"analysis":[123],"emotion":[125],"detection.":[126]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
