{"id":"https://openalex.org/W3135620065","doi":"https://doi.org/10.1109/access.2021.3064830","title":"A ConvBiLSTM Deep Learning Model-Based Approach for Twitter Sentiment Classification","display_name":"A ConvBiLSTM Deep Learning Model-Based Approach for Twitter Sentiment Classification","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3135620065","doi":"https://doi.org/10.1109/access.2021.3064830","mag":"3135620065"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3064830","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3064830","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09373371.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09373371.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007765220","display_name":"Tam Sakirin","orcid":"https://orcid.org/0000-0003-4103-1797"},"institutions":[{"id":"https://openalex.org/I3129675061","display_name":"Fatoni University","ror":"https://ror.org/01crwkz66","country_code":"TH","type":"education","lineage":["https://openalex.org/I3129675061"]},{"id":"https://openalex.org/I149218525","display_name":"Ankara University","ror":"https://ror.org/01wntqw50","country_code":"TR","type":"education","lineage":["https://openalex.org/I149218525"]}],"countries":["TH","TR"],"is_corresponding":true,"raw_author_name":"Sakirin Tam","raw_affiliation_strings":["Ankara University, Ankara, Turkey","Fatoni University, Panttani, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ankara University, Ankara, Turkey","institution_ids":["https://openalex.org/I149218525"]},{"raw_affiliation_string":"Fatoni University, Panttani, Thailand","institution_ids":["https://openalex.org/I3129675061"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029407732","display_name":"Rachid Ben Said","orcid":"https://orcid.org/0000-0003-0441-5548"},"institutions":[{"id":"https://openalex.org/I149218525","display_name":"Ankara University","ror":"https://ror.org/01wntqw50","country_code":"TR","type":"education","lineage":["https://openalex.org/I149218525"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Rachid Ben Said","raw_affiliation_strings":["Ankara University, Ankara, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ankara University, Ankara, Turkey","institution_ids":["https://openalex.org/I149218525"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034693671","display_name":"\u00d6mer \u00d6zg\u00fcr Tanr\u0131\u00f6ver","orcid":"https://orcid.org/0000-0002-1052-6124"},"institutions":[{"id":"https://openalex.org/I149218525","display_name":"Ankara University","ror":"https://ror.org/01wntqw50","country_code":"TR","type":"education","lineage":["https://openalex.org/I149218525"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"O. Ozgur Tanriover","raw_affiliation_strings":["Ankara University, Ankara, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ankara University, Ankara, Turkey","institution_ids":["https://openalex.org/I149218525"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5007765220"],"corresponding_institution_ids":["https://openalex.org/I149218525","https://openalex.org/I3129675061"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":13.5732,"has_fulltext":true,"cited_by_count":130,"citation_normalized_percentile":{"value":0.99066139,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"9","issue":null,"first_page":"41283","last_page":"41293"},"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9944000244140625,"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":0.9940999746322632,"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/word2vec","display_name":"Word2vec","score":0.8821223974227905},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8570175170898438},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.7946955561637878},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.7522395849227905},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7088223099708557},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6592717170715332},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6275513768196106},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5933428406715393},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5223511457443237},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5074829459190369},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.49540436267852783},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.48895370960235596},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4373566806316376},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.4275057315826416},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3743749260902405},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.35632461309432983}],"concepts":[{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.8821223974227905},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8570175170898438},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7946955561637878},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.7522395849227905},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7088223099708557},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6592717170715332},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6275513768196106},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5933428406715393},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5223511457443237},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5074829459190369},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.49540436267852783},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.48895370960235596},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4373566806316376},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.4275057315826416},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3743749260902405},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35632461309432983},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3064830","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3064830","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09373371.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:7182589cdbfb41b4a7f6b95398cf9ce3","is_oa":true,"landing_page_url":"https://doaj.org/article/7182589cdbfb41b4a7f6b95398cf9ce3","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 41283-41293 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3064830","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3064830","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09373371.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3135620065.pdf","grobid_xml":"https://content.openalex.org/works/W3135620065.grobid-xml"},"referenced_works_count":72,"referenced_works":["https://openalex.org/W71795751","https://openalex.org/W1499253590","https://openalex.org/W1526096287","https://openalex.org/W1614298861","https://openalex.org/W1832693441","https://openalex.org/W1889268436","https://openalex.org/W1936088391","https://openalex.org/W1992341207","https://openalex.org/W2025478229","https://openalex.org/W2076063813","https://openalex.org/W2110485445","https://openalex.org/W2112796928","https://openalex.org/W2114524997","https://openalex.org/W2120615054","https://openalex.org/W2131774270","https://openalex.org/W2147489358","https://openalex.org/W2153579005","https://openalex.org/W2156303437","https://openalex.org/W2165274630","https://openalex.org/W2208118460","https://openalex.org/W2250539671","https://openalex.org/W2251939518","https://openalex.org/W2265846598","https://openalex.org/W2284289336","https://openalex.org/W2407776548","https://openalex.org/W2472563954","https://openalex.org/W2514588627","https://openalex.org/W2556605533","https://openalex.org/W2586733088","https://openalex.org/W2604980468","https://openalex.org/W2610402245","https://openalex.org/W2755205424","https://openalex.org/W2760479033","https://openalex.org/W2768764839","https://openalex.org/W2784570262","https://openalex.org/W2901078833","https://openalex.org/W2903766561","https://openalex.org/W2914767245","https://openalex.org/W2941799245","https://openalex.org/W2949998441","https://openalex.org/W2950577311","https://openalex.org/W2951943225","https://openalex.org/W2963153906","https://openalex.org/W2963355447","https://openalex.org/W2964106094","https://openalex.org/W2964167669","https://openalex.org/W2985244827","https://openalex.org/W2993843842","https://openalex.org/W2996489182","https://openalex.org/W3022086662","https://openalex.org/W3032241892","https://openalex.org/W3088107006","https://openalex.org/W3146306708","https://openalex.org/W3152231500","https://openalex.org/W4211186029","https://openalex.org/W4233906183","https://openalex.org/W4254816979","https://openalex.org/W4294170691","https://openalex.org/W4403853345","https://openalex.org/W6631369687","https://openalex.org/W6636510571","https://openalex.org/W6639364127","https://openalex.org/W6682005813","https://openalex.org/W6682691769","https://openalex.org/W6691459498","https://openalex.org/W6693505360","https://openalex.org/W6695662000","https://openalex.org/W6713582272","https://openalex.org/W6764146914","https://openalex.org/W6764317799","https://openalex.org/W6793660297","https://openalex.org/W6986874642"],"related_works":["https://openalex.org/W3003606604","https://openalex.org/W2946409105","https://openalex.org/W2795129682","https://openalex.org/W3040974839","https://openalex.org/W3152932816","https://openalex.org/W2985392712","https://openalex.org/W4388996947","https://openalex.org/W3133567596","https://openalex.org/W2798009317","https://openalex.org/W4382201653"],"abstract_inverted_index":{"Being":[0],"one":[1],"of":[2,17,53,98,148,182,209],"the":[3,101,111,141,185,189,192,207,210,215,236],"most":[4],"widely":[5],"used":[6],"social":[7],"media":[8],"tools,":[9],"Twitter":[10,30],"is":[11,153,159],"seen":[12],"as":[13,176],"an":[14],"important":[15],"source":[16],"information":[18],"for":[19,136],"acquiring":[20],"people's":[21],"attitudes,":[22],"emotions,":[23],"views":[24],"and":[25,64,90,150,178,184,195,200,224],"feedbacks.":[26],"Within":[27],"this":[28,156],"context,":[29],"sentiment":[31,76,137],"analysis":[32,138],"techniques":[33],"were":[34,202],"developed":[35],"to":[36,49,109,114,205],"decide":[37],"whether":[38],"textual":[39],"tweets":[40,167],"express":[41],"a":[42,72,125,130,161],"positive":[43],"or":[44,133],"negative":[45],"opinion.":[46],"In":[47],"contrast":[48],"lower":[50],"classification":[51,143,197],"performance":[52],"traditional":[54],"algorithms,":[55,117],"deep":[56,115],"learning":[57,116],"models,":[58],"including":[59],"Convolution":[60],"Neural":[61],"Network":[62],"(CNN)":[63],"Bidirectional":[65],"Long":[66],"Short-Term":[67],"Memory":[68],"(Bi-LSTM),":[69],"have":[70],"achieved":[71],"significant":[73],"result":[74,213],"in":[75,124,155],"analysis.":[77],"Although":[78],"CNN":[79,132,149,171,193],"can":[80],"extract":[81,121],"high-level":[82],"local":[83,122],"features":[84,123],"efficiently":[85],"by":[86],"using":[87],"convolutional":[88],"layer":[89,172,194],"max-pooling":[91],"layer,":[92],"it":[93],"cannot":[94,120,139],"effectively":[95],"learn":[96],"sequence":[97],"correlations.":[99],"On":[100],"other":[102,237],"hand,":[103],"Bi-LSTM":[104,119,135,151,186],"uses":[105],"two":[106],"LSTM":[107],"directions":[108],"improve":[110],"contexts":[112],"available":[113],"but":[118],"parallel":[126],"way.":[127],"Therefore,":[128],"applying":[129],"single":[131,134],"achieve":[140],"optimal":[142],"result.":[144,198],"An":[145],"integrating":[146],"structure":[147],"model":[152,164,187,228],"proposed":[154,216],"study.":[157],"ConvBiLSTM":[158,218,227],"implemented;":[160],"word":[162,211],"embedding":[163,175,212],"which":[165],"converts":[166],"into":[168],"numerical":[169],"values,":[170],"receives":[173],"feature":[174],"input":[177,190],"produces":[179,196],"smaller":[180],"dimension":[181],"features,":[183],"takes":[188],"from":[191],"Word2Vec":[199,230],"GloVe":[201],"distinctly":[203],"applied":[204,220],"observe":[206],"impact":[208],"on":[214,231],"model.":[217],"was":[219],"with":[221,229,239],"retrieved":[222,232],"Tweets":[223,233],"SST-2":[225],"datasets.":[226],"dataset":[234],"outperformed":[235],"models":[238],"91.13%":[240],"accuracy.":[241]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":26},{"year":2024,"cited_by_count":31},{"year":2023,"cited_by_count":34},{"year":2022,"cited_by_count":28},{"year":2021,"cited_by_count":4}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}