{"id":"https://openalex.org/W4416158814","doi":"https://doi.org/10.48550/arxiv.2511.07077","title":"EmoBang: Detecting Emotion From Bengali Texts","display_name":"EmoBang: Detecting Emotion From Bengali Texts","publication_year":2025,"publication_date":"2025-11-10","ids":{"openalex":"https://openalex.org/W4416158814","doi":"https://doi.org/10.48550/arxiv.2511.07077"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2511.07077","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.07077","pdf_url":"https://arxiv.org/pdf/2511.07077","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.07077","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092522700","display_name":"Abdullah Al Maruf","orcid":"https://orcid.org/0000-0002-2202-552X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maruf, Abdullah Al","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113126937","display_name":"Aditi Golder","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Golder, Aditi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084883147","display_name":"Zakaria Masud Jiyad","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiyad, Zakaria Masud","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092522701","display_name":"Abdullah Al Numan","orcid":"https://orcid.org/0009-0006-4791-4151"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Numan, Abdullah Al","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5050415345","display_name":"Tarannum Shaila Zaman","orcid":"https://orcid.org/0000-0002-8634-524X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zaman, Tarannum Shaila","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":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9136999845504761,"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":0.9136999845504761,"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":0.03909999877214432,"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"}},{"id":"https://openalex.org/T12488","display_name":"Mental Health via Writing","score":0.0203000009059906,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social 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/bengali","display_name":"Bengali","score":0.9871000051498413},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4959999918937683},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.48730000853538513},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4620000123977661},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4162999987602234},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.4120999872684479},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.39340001344680786},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.37070000171661377}],"concepts":[{"id":"https://openalex.org/C19235068","wikidata":"https://www.wikidata.org/wiki/Q9610","display_name":"Bengali","level":2,"score":0.9871000051498413},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6819000244140625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6610999703407288},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5891000032424927},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4959999918937683},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48730000853538513},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4620000123977661},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4162999987602234},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.4120999872684479},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.39340001344680786},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.3515999913215637},{"id":"https://openalex.org/C2988148770","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion detection","level":3,"score":0.3395000100135803},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.335999995470047},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.31189998984336853},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30790001153945923},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2971999943256378},{"id":"https://openalex.org/C2776230583","wikidata":"https://www.wikidata.org/wiki/Q1322198","display_name":"Spoken language","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C206310091","wikidata":"https://www.wikidata.org/wiki/Q750859","display_name":"Emotion classification","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2511.07077","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.07077","pdf_url":"https://arxiv.org/pdf/2511.07077","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:mdsoar.org:11603/41409","is_oa":false,"landing_page_url":"https://doi.org/10.48550/arXiv.2511.07077","pdf_url":null,"source":{"id":"https://openalex.org/S4306402556","display_name":"Maryland Shared Open Access Repository (USMAI Consortium)","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},{"id":"doi:10.48550/arxiv.2511.07077","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.07077","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2511.07077","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.07077","pdf_url":"https://arxiv.org/pdf/2511.07077","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4416158814.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Emotion":[0],"detection":[1],"from":[2,115],"text":[3],"seeks":[4],"to":[5],"identify":[6],"an":[7,111],"individual's":[8],"emotional":[9],"or":[10,16],"mental":[11],"state":[12],"-":[13,18],"positive,":[14],"negative,":[15],"neutral":[17],"based":[19],"on":[20,141],"linguistic":[21],"cues.":[22],"While":[23],"significant":[24],"progress":[25],"has":[26],"been":[27],"made":[28],"for":[29,58,95,156,181],"English":[30],"and":[31,91,109,132,135,165,171,177],"other":[32],"high-resource":[33],"languages,":[34],"Bengali":[35,53,83,157],"remains":[36],"underexplored":[37],"despite":[38],"being":[39],"the":[40,142,145,152],"world's":[41],"fourth":[42],"most":[43],"spoken":[44],"language.":[45],"The":[46],"lack":[47],"of":[48,147,169],"large,":[49],"standardized":[50],"datasets":[51],"classifies":[52],"as":[54],"a":[55,81,100],"low-resource":[56],"language":[57,138],"emotion":[59,84,89,97,158],"detection.":[60,159],"Existing":[61],"studies":[62],"mainly":[63],"employ":[64],"classical":[65],"machine":[66],"learning":[67],"models":[68,94,126,139],"with":[69,127],"traditional":[70],"feature":[71,129],"engineering,":[72],"yielding":[73],"limited":[74],"performance.":[75],"In":[76],"this":[77,150],"paper,":[78],"we":[79,122],"introduce":[80],"new":[82],"dataset":[85],"annotated":[86],"across":[87],"eight":[88],"categories":[90],"propose":[92],"two":[93],"automatic":[96],"detection:":[98],"(i)":[99],"hybrid":[101],"Convolutional":[102],"Recurrent":[103],"Neural":[104],"Network":[105],"(CRNN)":[106],"model":[107,119],"(EmoBangHybrid)":[108],"(ii)":[110],"AdaBoost-Bidirectional":[112],"Encoder":[113],"Representations":[114],"Transformers":[116],"(BERT)":[117],"ensemble":[118],"(EmoBangEnsemble).":[120],"Additionally,":[121],"evaluate":[123],"six":[124],"baseline":[125],"five":[128],"engineering":[130],"techniques":[131],"assess":[133],"zero-shot":[134],"few-shot":[136],"large":[137],"(LLMs)":[140],"dataset.":[143],"To":[144],"best":[146],"our":[148],"knowledge,":[149],"is":[151],"first":[153],"comprehensive":[154],"benchmark":[155],"Experimental":[160],"results":[161],"show":[162],"that":[163],"EmoBangH":[164],"EmoBangE":[166],"achieve":[167],"accuracies":[168],"92.86%":[170],"93.69%,":[172],"respectively,":[173],"outperforming":[174],"existing":[175],"methods":[176],"establishing":[178],"strong":[179],"baselines":[180],"future":[182],"research.":[183]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-11-12T00:00:00"}
