{"id":"W4408015791","doi":"10.1007/978-981-96-1483-7_30","title":"Discovering Causal Relationships in Noisy Web Data for Sentiment Classification Using Attention Mechanisms","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Computer science; Sentiment analysis; Noisy data; Artificial intelligence; Natural language processing; Information retrieval; Data mining; Data science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004143533,0.0008021023,0.001054932,0.003180323,0.0007596449,0.002113607,0.001271864,0.001181447,0.003236322],"category_scores_gemma":[0.02052185,0.0006162261,0.001361922,0.002662305,0.0007170953,0.004528917,0.001365495,0.002457501,0.000663219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001136645,"about_ca_system_score_gemma":0.0009802314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002975412,"about_ca_topic_score_gemma":0.003111757,"domain_scores_codex":[0.9984462,0.0006082288,0.0001302718,0.0004219025,0.0002658154,0.0001276554],"domain_scores_gemma":[0.9844589,0.01301876,0.0009455298,0.0007816278,0.0006185535,0.0001765934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009423901,0.001263687,0.06087475,0.0007870885,0.0008696346,0.000989348,0.0007804273,0.1146399,0.02029704,0.08276752,0.01251885,0.7032695],"study_design_scores_gemma":[0.00002450598,0.00004741642,0.004104287,0.0000406671,0.0001313088,0.00006967264,0.00007269793,0.923408,0.00198319,0.06918897,0.0009117968,0.00001744582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1268536,0.001643456,0.8650951,0.001448765,0.0001459846,0.000180773,0.001004483,0.00122485,0.002402991],"genre_scores_gemma":[0.8577899,0.001050786,0.1369277,0.0002229457,0.0004091049,0.0002056051,0.00167709,0.0001008488,0.001615932],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004143533,"threshold_uncertainty_score":0.02191335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09704578524984683,"score_gpt":0.3170682906530183,"score_spread":0.2200225054031714,"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."}}