{"id":"W4410133712","doi":"10.1007/978-3-031-88653-9_63","title":"Efficient Aspect-Based Sentiment Analysis for Conversational Recommendation Based on a Distilled TinyBERT Model","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Sentiment analysis; Distilled water; Computer science; Natural language processing; Artificial intelligence; Chemistry; Chromatography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000625757,0.0003765438,0.0007242981,0.0007698814,0.0001603653,0.0002662336,0.0002584696,0.0003075031,0.00003897518],"category_scores_gemma":[0.00003264479,0.000329983,0.0003880701,0.0003383747,0.00002727741,0.00002377924,0.00005713135,0.0002393805,0.000001461568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001912063,"about_ca_system_score_gemma":0.0001010871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002144935,"about_ca_topic_score_gemma":0.00003885038,"domain_scores_codex":[0.997868,0.00006892865,0.0006270435,0.0008178636,0.0003518251,0.0002663405],"domain_scores_gemma":[0.9979325,0.001010408,0.0004067676,0.0004451396,0.0001331727,0.00007198959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003369808,0.00003025831,0.0002191666,0.00004385802,0.0003038853,0.000001252425,0.00003181252,0.9815819,5.09185e-7,0.01417821,0.0003360302,0.003239362],"study_design_scores_gemma":[0.0007191564,0.00005052216,0.00002549671,0.0003124728,0.0003190491,2.025653e-7,0.000001232093,0.9963007,0.000003094599,0.0003422163,0.001612789,0.0003130552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00001388007,0.0004056416,0.9931368,0.0007364902,0.0005369731,0.0006368982,0.00003497061,0.00004226379,0.00445609],"genre_scores_gemma":[0.9833491,0.0000183453,0.00882297,0.00106124,0.0003079722,0.0001328888,0.001235541,0.0000325383,0.005039386],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9843138,"threshold_uncertainty_score":0.9999152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01995841029969378,"score_gpt":0.2508587389444655,"score_spread":0.2309003286447717,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}