{"id":"https://openalex.org/W7134974350","doi":"https://doi.org/10.1109/icdmw69685.2025.00262","title":"Integrating LLM Sentiment Analysis into Machine Learning for Soccer Betting","display_name":"Integrating LLM Sentiment Analysis into Machine Learning for Soccer Betting","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W7134974350","doi":"https://doi.org/10.1109/icdmw69685.2025.00262"},"language":null,"primary_location":{"id":"doi:10.1109/icdmw69685.2025.00262","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw69685.2025.00262","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5095917452","display_name":"Samuel Samuel","orcid":null},"institutions":[{"id":"https://openalex.org/I79891267","display_name":"Singapore Management University","ror":"https://ror.org/050qmg959","country_code":"SG","type":"education","lineage":["https://openalex.org/I79891267"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Samuel Samuel","raw_affiliation_strings":["School of Computing and Information Systems, Singapore Management University,Singapore"],"affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, Singapore Management University,Singapore","institution_ids":["https://openalex.org/I79891267"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068938035","display_name":"Donghao Huang","orcid":"https://orcid.org/0009-0005-6767-4872"},"institutions":[{"id":"https://openalex.org/I79891267","display_name":"Singapore Management University","ror":"https://ror.org/050qmg959","country_code":"SG","type":"education","lineage":["https://openalex.org/I79891267"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Donghao Huang","raw_affiliation_strings":["School of Computing and Information Systems, Singapore Management University,Singapore"],"affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, Singapore Management University,Singapore","institution_ids":["https://openalex.org/I79891267"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128690798","display_name":"James Tan Yong Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I79891267","display_name":"Singapore Management University","ror":"https://ror.org/050qmg959","country_code":"SG","type":"education","lineage":["https://openalex.org/I79891267"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"James Tan Yong Hong","raw_affiliation_strings":["School of Computing and Information Systems, Singapore Management University,Singapore"],"affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, Singapore Management University,Singapore","institution_ids":["https://openalex.org/I79891267"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107892582","display_name":"Zhaoxia Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I79891267","display_name":"Singapore Management University","ror":"https://ror.org/050qmg959","country_code":"SG","type":"education","lineage":["https://openalex.org/I79891267"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zhaoxia Wang","raw_affiliation_strings":["School of Computing and Information Systems, Singapore Management University,Singapore"],"affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, Singapore Management University,Singapore","institution_ids":["https://openalex.org/I79891267"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5095917452"],"corresponding_institution_ids":["https://openalex.org/I79891267"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.88377303,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2156","last_page":"2164"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.2745000123977661,"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":0.2745000123977661,"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/T11674","display_name":"Sports Analytics and Performance","score":0.16949999332427979,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.051500000059604645,"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":0.3028999865055084},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.28999999165534973},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2768000066280365},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.2442999929189682},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.22529999911785126}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6674000024795532},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5609999895095825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5300999879837036},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.26089999079704285},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2442999929189682},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.22529999911785126},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.21860000491142273}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdmw69685.2025.00262","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw69685.2025.00262","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1995207790","https://openalex.org/W2034489173","https://openalex.org/W2336052368","https://openalex.org/W2606615623","https://openalex.org/W2911258580","https://openalex.org/W2999451755","https://openalex.org/W3094412822","https://openalex.org/W4360981072","https://openalex.org/W4389473931","https://openalex.org/W4392245580","https://openalex.org/W4402053208","https://openalex.org/W4403633914","https://openalex.org/W4406113679"],"related_works":[],"abstract_inverted_index":{"Traditional":[0],"sports":[1,176,211],"betting":[2,55,108,177],"models":[3,51,82,103,112],"have":[4],"primarily":[5],"relied":[6],"on":[7,90,190],"quantitative":[8],"statistics,":[9],"often":[10,222],"overlooking":[11],"the":[12,101,119,133,191,200],"predictive":[13],"power":[14],"of":[15,93,202],"qualitative":[16,207],"insights":[17],"such":[18],"as":[19,72],"sentiment":[20,33,64,126,145,184],"from":[21,66],"match":[22],"commentaries.":[23],"This":[24],"paper":[25],"presents":[26],"a":[27,58,129,148],"novel":[28],"hybrid":[29],"framework":[30],"that":[31,214],"integrates":[32],"features":[34,73,127,146,185],"generated":[35],"by":[36,124,224],"advanced":[37],"Large":[38],"Language":[39],"Models":[40],"(LLMs)-specifically":[41],"DeepSeek-R1,":[42],"GPT-4o":[43],"mini,":[44],"GPT-4.1,":[45],"and":[46,69,80,86,132,161,181,194,206],"Qwen2.5-Plus-into":[47],"conventional":[48],"machine":[49],"learning":[50],"to":[52,99],"enhance":[53],"soccer":[54],"prediction.":[56],"Using":[57],"zero-shot":[59],"prompting":[60],"approach,":[61],"we":[62],"extract":[63],"ratings":[65],"post-match":[67],"commentaries":[68],"incorporate":[70],"them":[71],"into":[74],"logistic":[75,140],"regression,":[76],"SVC,":[77],"random":[78],"forest,":[79],"XGBoost":[81,134],"under":[83],"both":[84,203],"accuracy-":[85],"F1-tuned":[87,111,139],"configurations.":[88],"Evaluated":[89],"nine":[91],"seasons":[92],"English":[94],"Premier":[95],"League":[96],"data":[97,208],"(2015-16":[98],"2023-24),":[100],"integrated":[102],"demonstrate":[104],"substantial":[105],"improvements":[106],"in":[107,210],"profitability.":[109],"Notably,":[110],"consistently":[113],"outperform":[114],"their":[115],"accuracy-tuned":[116],"counterparts,":[117],"with":[118,143,174],"Random":[120],"Forest":[121],"model":[122,142,159,232],"enhanced":[123],"GPT-4.1":[125],"achieving":[128],"279.48%":[130],"return,":[131,150],"baseline":[135],"reaching":[136],"247.97%.":[137],"The":[138],"regression":[141],"DeepSeekR1":[144],"yielded":[147],"174.01%":[149],"significantly":[151],"surpassing":[152],"traditional":[153,225],"approaches.":[154],"Our":[155],"comprehensive":[156],"evaluation":[157],"across":[158],"architectures":[160],"tuning":[162,195],"strategies":[163],"reveals":[164],"two":[165],"key":[166],"findings:":[167],"(1)":[168],"F1":[169],"optimization":[170,204],"is":[171],"better":[172],"aligned":[173],"profitable":[175],"than":[178],"accuracy":[179],"optimization,":[180],"(2)":[182],"LLM-derived":[183],"provide":[186],"varying":[187],"benefits":[188],"depending":[189],"underlying":[192],"algorithm":[193],"method.":[196],"These":[197],"results":[198],"underscore":[199],"importance":[201],"strategy":[205],"integration":[209],"analytics,":[212],"demonstrating":[213],"pre-trained":[215],"LLMs":[216],"can":[217],"effectively":[218],"capture":[219],"latent":[220],"factors":[221],"missed":[223],"statistical":[226],"methods,":[227],"all":[228],"without":[229],"requiring":[230],"additional":[231],"training.":[233]},"counts_by_year":[],"updated_date":"2026-03-13T14:20:09.374765","created_date":"2026-03-12T00:00:00"}