{"id":"https://openalex.org/W7081574438","doi":"https://doi.org/10.1051/itmconf/20257804023/pdf","title":"Bertmucb: An Efficient Approach for Sentiment Analysis","display_name":"Bertmucb: An Efficient Approach for Sentiment Analysis","publication_year":2025,"publication_date":"2025-09-08","ids":{"openalex":"https://openalex.org/W7081574438","doi":"https://doi.org/10.1051/itmconf/20257804023/pdf"},"language":"en","primary_location":{"id":"pmh:oai:edpsciences.org:dkey/10.1051/itmconf/20257804023","doi":"https://doi.org/10.1051/itmconf/20257804023/pdf","is_oa":false,"landing_page_url":"https://www.itm-conferences.org/10.1051/itmconf/20257804023/pdf","pdf_url":"https://www.itm-conferences.org/10.1051/itmconf/20257804023/pdf","source":{"id":"https://openalex.org/S4306400744","display_name":"Springer Link (Chiba Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I8488066","host_organization_name":"Chiba Institute of Technology","host_organization_lineage":["https://openalex.org/I8488066"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1051/itmconf/20257804023","raw_type":"Text"},"volume":null,"type":"article","indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Bingjia Huang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bingjia Huang","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3616301,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":1.0,"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":1.0,"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":0.0,"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/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.0,"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":0.9789999723434448},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"reinforcement learning","score":0.6259999871253967},{"id":"https://openalex.org/keywords/upper-confidence-bound","display_name":"upper confidence bound","score":0.9049999713897705},{"id":"https://openalex.org/keywords/pid-controller","display_name":"PID controller","score":0.7289999723434448},{"id":"https://openalex.org/keywords/roberta-fine-tuning","display_name":"RoBERTa fine-tuning","score":0.7839999794960022},{"id":"https://openalex.org/keywords/multi-layer-perceptron-classifier","display_name":"multi-layer perceptron classifier","score":0.8730000257492065},{"id":"https://openalex.org/keywords/fine-grained-sentiment","display_name":"fine-grained sentiment","score":0.6790000200271606}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7918999791145325},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6905999779701233},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6676999926567078},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6340000033378601},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5529000163078308},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5460000038146973},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5209000110626221},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.47850000858306885},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4717999994754791},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.3124000132083893},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28060001134872437},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2651999890804291},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:edpsciences.org:dkey/10.1051/itmconf/20257804023","doi":"https://doi.org/10.1051/itmconf/20257804023/pdf","is_oa":false,"landing_page_url":"https://www.itm-conferences.org/10.1051/itmconf/20257804023/pdf","pdf_url":"https://www.itm-conferences.org/10.1051/itmconf/20257804023/pdf","source":{"id":"https://openalex.org/S4306400744","display_name":"Springer Link (Chiba Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I8488066","host_organization_name":"Chiba Institute of Technology","host_organization_lineage":["https://openalex.org/I8488066"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1051/itmconf/20257804023","raw_type":"Text"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"research":[1,32],"has":[2],"made":[3],"huge":[4],"progress":[5],"in":[6,12,108],"sentiment":[7,38],"analysis,":[8],"however,":[9],"challenges":[10],"remain":[11],"balancing":[13],"model":[14,74],"complexity":[15],"with":[16,75],"performance,":[17,58],"also":[18,60],"how":[19],"to":[20,23,47,55,101,111,129,148,158],"train":[21],"models":[22,146],"better":[24,57,112],"fit":[25,113],"into":[26,66],"conversations":[27],"is":[28,99,156],"another":[29],"problem.":[30],"This":[31],"proposes":[33],"a":[34,72,76,89,95,122],"novel":[35],"algorithm":[36,128],"for":[37],"analysis":[39],"called":[40],"BertmUCB.":[41],"It":[42],"leverages":[43],"Reinforcement":[44],"learning's":[45],"capability":[46],"exploit":[48],"historical":[49],"feedback":[50],"and":[51,59,152],"language":[52,139,145],"models'":[53],"efficiency":[54],"gain":[56],"considers":[61],"the":[62,85,102,119,131,135,160],"feasibility":[63],"of":[64,84,162],"deploying":[65],"reality.":[67],"The":[68],"author":[69,120],"first":[70],"fine-tunes":[71],"RoBERTa":[73],"two-layer":[77],"Multi-Layer":[78],"Perceptron":[79],"(MLP)":[80],"classifier":[81],"on":[82,142],"top":[83],"simplyweibo_4_moods":[86],"dataset,":[87],"then,":[88],"prediction":[90,109,117],"enhancement":[91,110],"method":[92],"optimized":[93],"by":[94],"Proportion-Integration-Differentiation":[96],"(PID)":[97],"controller":[98],"applied":[100],"MLP's":[103],"output.":[104],"PID":[105],"adapts":[106],"parameters":[107],"different":[114],"datasets.":[115],"After":[116],"enhancement,":[118],"uses":[121],"modified":[123],"Upper":[124],"Confidence":[125],"Bound":[126],"(UCB)":[127],"perform":[130],"arm":[132],"selection.":[133],"In":[134],"experiment,":[136],"BertmUCB":[137],"outperforms":[138],"models,":[140],"especially":[141],"datasets":[143],"where":[144],"struggle":[147],"distinguish":[149],"fine-grained":[150],"sentiments,":[151],"an":[153],"ablation":[154],"study":[155],"conducted":[157],"quantify":[159],"contribution":[161],"each":[163],"module.":[164]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2025-10-10T00:00:00"}
