{"id":"https://openalex.org/W4415332488","doi":"https://doi.org/10.48550/arxiv.2509.23515","title":"From Human Annotation to Automation: LLM-in-the-Loop Active Learning for Arabic Sentiment Analysis","display_name":"From Human Annotation to Automation: LLM-in-the-Loop Active Learning for Arabic Sentiment Analysis","publication_year":2025,"publication_date":"2025-09-27","ids":{"openalex":"https://openalex.org/W4415332488","doi":"https://doi.org/10.48550/arxiv.2509.23515"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2509.23515","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.23515","pdf_url":"https://arxiv.org/pdf/2509.23515","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"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"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.23515","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084756913","display_name":"Dania Refai","orcid":"https://orcid.org/0000-0002-4599-8797"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Refai, Dania","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119756121","display_name":"Alaa Dalaq","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dalaq, Alaa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120056882","display_name":"Doaa Dalaq","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dalaq, Doaa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5070341092","display_name":"Irfan Ahmad","orcid":"https://orcid.org/0000-0001-8311-1731"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmad, Irfan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.90420001745224,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.90420001745224,"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/annotation","display_name":"Annotation","score":0.871399998664856},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6556000113487244},{"id":"https://openalex.org/keywords/arabic","display_name":"Arabic","score":0.5680000185966492},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5496000051498413},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.46050000190734863}],"concepts":[{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.871399998664856},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7768999934196472},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6858000159263611},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6845999956130981},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6556000113487244},{"id":"https://openalex.org/C96455323","wikidata":"https://www.wikidata.org/wiki/Q13955","display_name":"Arabic","level":2,"score":0.5680000185966492},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5496000051498413},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.46050000190734863},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.426800012588501},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27810001373291016},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2775999903678894}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2509.23515","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.23515","pdf_url":"https://arxiv.org/pdf/2509.23515","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"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.2509.23515","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.23515","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.23515","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.23515","pdf_url":"https://arxiv.org/pdf/2509.23515","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Natural":[0],"language":[1,73],"processing":[2],"(NLP),":[3],"particularly":[4],"sentiment":[5,33,66,105],"analysis,":[6],"plays":[7],"a":[8],"vital":[9],"role":[10],"in":[11,31,52,56,64,90],"areas":[12],"like":[13],"marketing,":[14],"customer":[15],"service,":[16],"and":[17,27,79,131,143,150,161,179,201],"social":[18],"media":[19],"monitoring":[20],"by":[21],"providing":[22],"insights":[23],"into":[24],"user":[25],"opinions":[26],"emotions.":[28],"However,":[29],"progress":[30],"Arabic":[32,65,92,104,149],"analysis":[34,106],"remains":[35],"limited":[36],"due":[37],"to":[38,83,108,219],"the":[39,69,91,187,225,229,245,258],"lack":[40],"of":[41,71],"large,":[42],"high-quality":[43],"labeled":[44,238,255],"datasets.":[45],"While":[46],"active":[47,100,211],"learning":[48,101,120,212],"has":[49],"proven":[50],"effective":[51],"reducing":[53],"annotation":[54,78,110,155],"efforts":[55],"other":[57],"languages,":[58],"few":[59],"studies":[60],"have":[61],"explored":[62],"it":[63],"tasks.":[67],"Likewise,":[68],"use":[70],"large":[72],"models":[74],"(LLMs)":[75],"for":[76,103,193,199,204],"assisting":[77],"comparing":[80],"their":[81],"performance":[82,217],"human":[84,220,262],"labeling":[85,160],"is":[86],"still":[87],"largely":[88],"unexplored":[89],"context.":[93],"In":[94],"this":[95],"paper,":[96],"we":[97],"propose":[98],"an":[99],"framework":[102],"designed":[107],"reduce":[109],"costs":[111],"while":[112,243],"maintaining":[113],"high":[114],"performance.":[115],"We":[116],"evaluate":[117],"multiple":[118],"deep":[119],"architectures:":[121],"Specifically,":[122],"long":[123],"short-term":[124],"memory":[125],"(LSTM),":[126],"gated":[127],"recurrent":[128,132],"units":[129],"(GRU),":[130],"neural":[133],"networks":[134],"(RNN),":[135],"across":[136],"three":[137],"benchmark":[138],"datasets:":[139],"Hunger":[140,194,226],"Station,":[141,195],"AJGT,":[142,200],"MASAC,":[144],"encompassing":[145],"both":[146],"modern":[147],"standard":[148],"dialectal":[151],"variations.":[152],"Additionally,":[153],"two":[154],"strategies":[156],"are":[157,166],"compared:":[158],"Human":[159],"LLM-assisted":[162,210],"labeling.":[163,221,263],"Five":[164],"LLMs":[165],"evaluated":[167],"as":[168],"annotators:":[169],"GPT-4o,":[170],"Claude":[171,196],"3":[172,181,197],"Sonnet,":[173],"Gemini":[174],"2.5":[175],"Pro,":[176],"DeepSeek":[177,202,248],"Chat,":[178],"LLaMA":[180],"70B":[182],"Instruct.":[183],"For":[184,222],"each":[185],"dataset,":[186,228,247],"best-performing":[188],"LLM":[189],"was":[190],"used:":[191],"GPT-4o":[192],"Sonnet":[198],"Chat":[203,249],"MASAC.":[205],"Our":[206],"results":[207],"show":[208],"that":[209],"achieves":[213],"competitive":[214],"or":[215],"superior":[216],"compared":[218],"example,":[223],"on":[224,244],"Station":[227],"LSTM":[230],"model":[231],"achieved":[232],"93%":[233],"accuracy":[234,252,259],"with":[235,253],"only":[236],"450":[237],"samples":[239],"using":[240],"GPT-4o-generated":[241],"labels,":[242],"MASAC":[246],"reached":[250],"82%":[251],"650":[254],"samples,":[256],"matching":[257],"obtained":[260],"through":[261]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-19T00:00:00"}