{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001882923,0.0003464589,0.0004092375,0.001272971,0.0003527412,0.0006480269,0.002529285,0.0002231263,0.000006427377],"category_scores_gemma":[0.0001135857,0.000350852,0.0001088835,0.0008480125,0.0001206758,0.001127596,0.001637172,0.0004677215,0.000006103743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004467799,"about_ca_system_score_gemma":0.0004931946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002547762,"about_ca_topic_score_gemma":0.0002098299,"domain_scores_codex":[0.9964435,0.00005902149,0.0006981113,0.00167526,0.0006870489,0.0004370268],"domain_scores_gemma":[0.9972458,0.0004263646,0.0003853212,0.001735282,0.0001366164,0.00007061558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002760179,0.0001961332,0.002467159,0.0002563299,0.0001321876,0.00003746107,0.00106565,0.2285077,0.01131499,0.4275314,0.0001532226,0.3283102],"study_design_scores_gemma":[0.0002683614,0.00002501425,0.0003966959,0.0005300604,0.00002565309,0.000004123876,0.000001384022,0.9664727,0.000215849,0.03143166,0.0002871282,0.0003413572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003382605,0.0001393789,0.9965435,0.0005960451,0.00147884,0.0005136142,0.00002120671,0.00005498233,0.0003141848],"genre_scores_gemma":[0.1782531,0.00003150001,0.8205492,0.0001984862,0.000237615,0.00001537824,0.0002077034,0.00002368099,0.0004833403],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.737965,"threshold_uncertainty_score":0.9998943,"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."}}