{"id":"https://openalex.org/W2982645239","doi":"https://doi.org/10.1109/bcd.2019.8885108","title":"Multimodal Sentiment Analysis via RNN variants","display_name":"Multimodal Sentiment Analysis via RNN variants","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2982645239","doi":"https://doi.org/10.1109/bcd.2019.8885108","mag":"2982645239"},"language":"en","primary_location":{"id":"doi:10.1109/bcd.2019.8885108","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bcd.2019.8885108","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data, Cloud Computing, Data Science &amp; Engineering (BCD)","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/A5112724938","display_name":"Ayush Agarwal","orcid":null},"institutions":[{"id":"https://openalex.org/I863896202","display_name":"Delhi Technological University","ror":"https://ror.org/01ztcvt22","country_code":"IN","type":"education","lineage":["https://openalex.org/I863896202"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ayush Agarwal","raw_affiliation_strings":["Department of Information Technology, Delhi Technological University, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Technology, Delhi Technological University, New Delhi, India","institution_ids":["https://openalex.org/I863896202"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043006424","display_name":"Ashima Yadav","orcid":"https://orcid.org/0000-0002-1467-1601"},"institutions":[{"id":"https://openalex.org/I863896202","display_name":"Delhi Technological University","ror":"https://ror.org/01ztcvt22","country_code":"IN","type":"education","lineage":["https://openalex.org/I863896202"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ashima Yadav","raw_affiliation_strings":["Department of Information Technology, Delhi Technological University, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Technology, Delhi Technological University, New Delhi, India","institution_ids":["https://openalex.org/I863896202"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021449557","display_name":"Dinesh Kumar Vishwakarma","orcid":"https://orcid.org/0000-0002-1026-0047"},"institutions":[{"id":"https://openalex.org/I863896202","display_name":"Delhi Technological University","ror":"https://ror.org/01ztcvt22","country_code":"IN","type":"education","lineage":["https://openalex.org/I863896202"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dinesh Kumar Vishwakarma","raw_affiliation_strings":["Department of Information Technology, Delhi Technological University, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Technology, Delhi Technological University, New Delhi, India","institution_ids":["https://openalex.org/I863896202"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.772,"has_fulltext":false,"cited_by_count":60,"citation_normalized_percentile":{"value":0.96012864,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"19","last_page":"23"},"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":0.995199978351593,"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":0.9904999732971191,"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/sentiment-analysis","display_name":"Sentiment analysis","score":0.8800605535507202},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8488531112670898},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.7014075517654419},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.655604362487793},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5071396827697754},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.41370105743408203},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.33965563774108887}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.8800605535507202},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8488531112670898},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.7014075517654419},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.655604362487793},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5071396827697754},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.41370105743408203},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.33965563774108887},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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/bcd.2019.8885108","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bcd.2019.8885108","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data, Cloud Computing, Data Science &amp; Engineering (BCD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.550000011920929}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1591801644","https://openalex.org/W2019759670","https://openalex.org/W2035265584","https://openalex.org/W2465534249","https://openalex.org/W2556418146","https://openalex.org/W2556605533","https://openalex.org/W2557270725","https://openalex.org/W2569259664","https://openalex.org/W2605203995","https://openalex.org/W2739413291","https://openalex.org/W2740550900","https://openalex.org/W2743699808","https://openalex.org/W2754974968","https://openalex.org/W2764130664","https://openalex.org/W2766306366","https://openalex.org/W2767249564","https://openalex.org/W2768787743","https://openalex.org/W2771841020","https://openalex.org/W2772633765","https://openalex.org/W2774683704","https://openalex.org/W2786749325","https://openalex.org/W2787037604","https://openalex.org/W2800709752","https://openalex.org/W2802835792","https://openalex.org/W2804777280","https://openalex.org/W2805121932","https://openalex.org/W2810083390","https://openalex.org/W2889702481","https://openalex.org/W2891679674","https://openalex.org/W2950059857","https://openalex.org/W2964121744","https://openalex.org/W2964260444","https://openalex.org/W6631190155","https://openalex.org/W6635446068","https://openalex.org/W6731748852","https://openalex.org/W6736314328","https://openalex.org/W6741840471","https://openalex.org/W6752293769"],"related_works":["https://openalex.org/W2548633793","https://openalex.org/W3089396779","https://openalex.org/W3132372214","https://openalex.org/W2596247554","https://openalex.org/W4301373556","https://openalex.org/W2941935829","https://openalex.org/W2438765327","https://openalex.org/W3013279174","https://openalex.org/W4317653575","https://openalex.org/W4224284088"],"abstract_inverted_index":{"Multimodal":[0,29],"sentiment":[1,7,21,30,49,91,125],"analysis":[2,22,31],"involves":[3],"the":[4,36,40,69,72,75,105,117],"classification":[5,92],"of":[6,12,59,71],"by":[8],"using":[9,43,120],"different":[10,57],"forms":[11],"data":[13],"together,":[14],"namely,":[15,61],"text,":[16],"audio,":[17,98],"and":[18,42,65,99],"video.":[19],"Previously":[20],"was":[23],"implemented":[24],"only":[25],"on":[26,34,79,94,104],"textual":[27],"data.":[28],"relies":[32],"mainly":[33],"identifying":[35],"utterances":[37,70],"present":[38],"in":[39],"video":[41],"them":[44],"as":[45],"a":[46],"basis":[47],"for":[48,67,123],"classification.":[50],"In":[51],"this":[52],"paper,":[53],"we":[54],"proposed":[55],"four":[56],"variants":[58],"RNN,":[60],"GRNN,":[62],"LRNN,":[63],"GLRNN":[64],"UGRNN":[66],"analyzing":[68],"speakers":[73],"from":[74],"videos.":[76],"Experimental":[77],"results":[78,114],"CMI-MOSI":[80],"dataset":[81],"demonstrates":[82],"that":[83],"our":[84,109],"approach":[85],"is":[86],"able":[87],"to":[88],"achieve":[89],"better":[90],"accuracy":[93],"individual":[95,118],"modality":[96,119],"(text,":[97],"video)":[100],"than":[101],"existing":[102],"approaches":[103],"same":[106],"dataset.":[107],"Moreover,":[108],"method":[110],"also":[111],"gave":[112],"decent":[113],"after":[115],"fusing":[116],"Attention":[121],"networks":[122],"multimodal":[124],"analysis.":[126]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":16},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
