{"id":"https://openalex.org/W3116001339","doi":"https://doi.org/10.14704/web/v17i2/web17059","title":"A Hybrid Deep Learning Model for Long-Term Sentiment Classification","display_name":"A Hybrid Deep Learning Model for Long-Term Sentiment Classification","publication_year":2020,"publication_date":"2020-12-21","ids":{"openalex":"https://openalex.org/W3116001339","doi":"https://doi.org/10.14704/web/v17i2/web17059","mag":"3116001339"},"language":"en","primary_location":{"id":"doi:10.14704/web/v17i2/web17059","is_oa":true,"landing_page_url":"https://doi.org/10.14704/web/v17i2/web17059","pdf_url":null,"source":{"id":"https://openalex.org/S4210195749","display_name":"Webology","issn_l":"1735-188X","issn":["1735-188X"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310318996","host_organization_name":"University of Tehran Press","host_organization_lineage":["https://openalex.org/P4310318996"],"host_organization_lineage_names":["University of Tehran Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Webology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.14704/web/v17i2/web17059","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007224304","display_name":"Tapas Guha","orcid":"https://orcid.org/0000-0002-3641-3865"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tapas Guha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5110688767","display_name":"K. G. Mohan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"K.G. Mohan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2608,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.65303199,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"17","issue":"2","first_page":"663","last_page":"676"},"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/T10028","display_name":"Topic Modeling","score":0.9988999962806702,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9980000257492065,"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":0.8542965054512024},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8356509208679199},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7562188506126404},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6878505945205688},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6060371994972229},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5996953248977661},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.517233669757843},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.44894811511039734},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3364468812942505},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.33207812905311584}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.8542965054512024},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8356509208679199},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7562188506126404},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6878505945205688},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6060371994972229},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5996953248977661},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.517233669757843},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.44894811511039734},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3364468812942505},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33207812905311584},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14704/web/v17i2/web17059","is_oa":true,"landing_page_url":"https://doi.org/10.14704/web/v17i2/web17059","pdf_url":null,"source":{"id":"https://openalex.org/S4210195749","display_name":"Webology","issn_l":"1735-188X","issn":["1735-188X"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310318996","host_organization_name":"University of Tehran Press","host_organization_lineage":["https://openalex.org/P4310318996"],"host_organization_lineage_names":["University of Tehran Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Webology","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.14704/web/v17i2/web17059","is_oa":true,"landing_page_url":"https://doi.org/10.14704/web/v17i2/web17059","pdf_url":null,"source":{"id":"https://openalex.org/S4210195749","display_name":"Webology","issn_l":"1735-188X","issn":["1735-188X"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310318996","host_organization_name":"University of Tehran Press","host_organization_lineage":["https://openalex.org/P4310318996"],"host_organization_lineage_names":["University of Tehran Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Webology","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W179875071","https://openalex.org/W1832693441","https://openalex.org/W1889268436","https://openalex.org/W2033310064","https://openalex.org/W2064675550","https://openalex.org/W2069143585","https://openalex.org/W2079735306","https://openalex.org/W2107878631","https://openalex.org/W2112796928","https://openalex.org/W2153579005","https://openalex.org/W2157331557","https://openalex.org/W2158899491","https://openalex.org/W2163605009","https://openalex.org/W2166706824","https://openalex.org/W2170973209","https://openalex.org/W2250966211","https://openalex.org/W2252335727","https://openalex.org/W2253891449","https://openalex.org/W2584429674","https://openalex.org/W2963012544","https://openalex.org/W2963355447","https://openalex.org/W2964236337","https://openalex.org/W3049711450","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W4297676672","https://openalex.org/W3099765033","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"With":[0],"the":[1,15,44,76,136],"omnipresence":[2],"of":[3,10,35,43,78,98,140,180],"user":[4],"feedbacks":[5],"in":[6,37,75,89,117],"social":[7],"media,":[8],"mining":[9],"relevant":[11],"opinion":[12],"and":[13,47,103,131,142,161,178,186],"extracting":[14],"underlying":[16],"sentiment":[17,49,90,123,154],"to":[18,120,134,143],"analyze":[19],"synthetic":[20],"emotion":[21],"towards":[22,87],"a":[23,32,169],"specific":[24],"product,":[25],"person,":[26],"topic":[27],"or":[28,62],"event":[29],"has":[30,83,148],"become":[31],"vast":[33],"domain":[34],"research":[36],"recent":[38],"times.":[39],"A":[40,92],"thorough":[41],"survey":[42],"early":[45],"unimodal":[46],"multimodal":[48],"classification":[50],"approaches":[51],"reveals":[52],"that":[53],"researchers":[54],"mostly":[55],"relied":[56],"on":[57,65,151],"either":[58],"corpus":[59],"based":[60,64],"techniques":[61,133],"those":[63],"machine":[66],"learning":[67,71,95],"algorithms.":[68],"Lately,":[69],"Deep":[70],"models":[72,174],"progressed":[73],"profoundly":[74],"area":[77],"image":[79],"processing.":[80],"This":[81,125],"success":[82],"been":[84,149],"efficiently":[85],"directed":[86],"enhancements":[88],"categorization.":[91],"hybrid":[93],"deep":[94],"model":[96,137],"consisting":[97],"Convolutional":[99],"Neural":[100],"Network":[101],"(CNN)":[102],"stacked":[104],"bidirectional":[105],"Long":[106],"Short":[107],"Term":[108],"Memory":[109],"(BiLSTM)":[110],"over":[111,172],"pre-trained":[112],"word":[113],"vectors":[114],"is":[115],"proposed":[116],"this":[118],"paper":[119],"achieve":[121,144],"long-term":[122],"analysis.":[124],"work":[126],"experiments":[127],"with":[128],"various":[129],"hyperparameters":[130],"optimization":[132],"make":[135],"get":[138],"rid":[139],"overfitting":[141],"optimal":[145],"performance.":[146],"It":[147,167],"validated":[150],"two":[152],"standard":[153],"datasets,":[155],"Stanford":[156,162],"Large":[157],"Movie":[158],"Review":[159],"(IMDB)":[160],"Sentiment":[163],"Treebank2":[164],"Dataset":[165],"(SST2).":[166],"achieves":[168],"competitive":[170],"advantage":[171],"other":[173],"like":[175],"CNN,":[176],"LSTM":[177],"ensemble":[179],"CNN-LSTM":[181],"by":[182],"attaining":[183],"better":[184],"accuracy":[185],"also":[187],"produces":[188],"high":[189],"F":[190],"measure.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
