{"id":"https://openalex.org/W2555018403","doi":"https://doi.org/10.1109/ialp.2016.7875989","title":"Valence-arousal ratings prediction with co-occurrence word-embedding","display_name":"Valence-arousal ratings prediction with co-occurrence word-embedding","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2555018403","doi":"https://doi.org/10.1109/ialp.2016.7875989","mag":"2555018403"},"language":"en","primary_location":{"id":"doi:10.1109/ialp.2016.7875989","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ialp.2016.7875989","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Asian Language Processing (IALP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086261973","display_name":"Yan Gao","orcid":"https://orcid.org/0000-0002-8426-6724"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yan Gao","raw_affiliation_strings":["Research and Development, SAS Research & Development (Beijing) Co Ltd, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Research and Development, SAS Research & Development (Beijing) Co Ltd, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100729478","display_name":"Xu Yang","orcid":"https://orcid.org/0000-0002-0405-6816"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu Yang","raw_affiliation_strings":["Research and Development, SAS Research & Development (Beijing) Co Ltd, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Research and Development, SAS Research & Development (Beijing) Co Ltd, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042066408","display_name":"Jing Xu","orcid":"https://orcid.org/0000-0001-8532-2241"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jing Xu","raw_affiliation_strings":["Research and Development, SAS Research & Development (Beijing) Co Ltd, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Research and Development, SAS Research & Development (Beijing) Co Ltd, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101711431","display_name":"Bin Xu","orcid":"https://orcid.org/0000-0002-4117-6833"},"institutions":[{"id":"https://openalex.org/I122754148","display_name":"SAS Institute (United States)","ror":"https://ror.org/01093z329","country_code":"US","type":"company","lineage":["https://openalex.org/I122754148"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bin Xu","raw_affiliation_strings":["Advanced Analytics R&D, Text Analytics SAS Institute Inc, Cary, America"],"affiliations":[{"raw_affiliation_string":"Advanced Analytics R&D, Text Analytics SAS Institute Inc, Cary, America","institution_ids":["https://openalex.org/I122754148"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5086261973"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4285,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.79700029,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"7","issue":null,"first_page":"293","last_page":"296"},"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":0.9976999759674072,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/valence","display_name":"Valence (chemistry)","score":0.8537551760673523},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.8049492835998535},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.7546611428260803},{"id":"https://openalex.org/keywords/arousal","display_name":"Arousal","score":0.7486146092414856},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.675865113735199},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6738790273666382},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6660881638526917},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5264385938644409},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5248354077339172},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4832685589790344},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.35205864906311035},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.314214289188385},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.24196869134902954},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17962804436683655},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.0907493531703949}],"concepts":[{"id":"https://openalex.org/C168900304","wikidata":"https://www.wikidata.org/wiki/Q171407","display_name":"Valence (chemistry)","level":2,"score":0.8537551760673523},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.8049492835998535},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7546611428260803},{"id":"https://openalex.org/C36951298","wikidata":"https://www.wikidata.org/wiki/Q379784","display_name":"Arousal","level":2,"score":0.7486146092414856},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.675865113735199},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6738790273666382},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6660881638526917},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5264385938644409},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5248354077339172},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4832685589790344},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.35205864906311035},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.314214289188385},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.24196869134902954},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17962804436683655},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0907493531703949},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ialp.2016.7875989","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ialp.2016.7875989","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Asian Language Processing (IALP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W947140380","https://openalex.org/W1966797434","https://openalex.org/W2149628368","https://openalex.org/W2250539671","https://openalex.org/W2250879510","https://openalex.org/W2276596416","https://openalex.org/W2468785836","https://openalex.org/W6624822662","https://openalex.org/W6691399001","https://openalex.org/W6694333015"],"related_works":["https://openalex.org/W2029072726","https://openalex.org/W91913183","https://openalex.org/W2936882366","https://openalex.org/W2736893848","https://openalex.org/W2128698257","https://openalex.org/W1544055438","https://openalex.org/W3003450285","https://openalex.org/W2013608943","https://openalex.org/W4399628019","https://openalex.org/W2085024878"],"abstract_inverted_index":{"Dimensional":[0],"approach":[1,39,62],"has":[2],"become":[3],"a":[4],"popular":[5],"method":[6],"in":[7,18,44,66],"sentiment":[8,33],"analysis":[9,34],"because":[10],"it":[11,28,59],"represents":[12,41],"emotions":[13],"as":[14,22],"continuous":[15],"numerical":[16],"values":[17],"multiple":[19],"dimensions,":[20],"such":[21],"valence-arousal":[23],"(VA)":[24],"space":[25],"[1],":[26],"therefore":[27],"can":[29],"provide":[30],"more":[31],"fine-grained":[32],"compared":[35],"to":[36,64,74],"traditional":[37],"categorical":[38],"which":[40],"affective":[42,48,80,88],"states":[43],"discrete":[45],"classes.":[46],"However,":[47],"lexicons":[49,81],"and":[50,58,86],"corpora":[51,89],"with":[52,90],"VA":[53,76,91],"ratings":[54,77],"are":[55],"very":[56],"rare":[57],"makes":[60],"dimensional":[61],"hard":[63],"use":[65],"reality.":[67],"This":[68],"paper":[69],"describes":[70],"using":[71],"KNN":[72],"algorithm":[73],"predict":[75],"for":[78],"new":[79],"by":[82],"leveraging":[83],"word":[84],"embedding":[85],"available":[87],"ratings.":[92]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}