{"id":"W4408473356","doi":"10.1145/3723355","title":"Enhancing Log Sentiments: An Exploratory Study of Sentiments and Emotions with Software Logs","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Exploratory research; Data science; Software; World Wide Web; Programming language; Sociology","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":[0.001716481,0.0003443166,0.000326274,0.001214835,0.0006940071,0.001326289,0.0003935761,0.0005385564,0.0006675928],"category_scores_gemma":[0.01025221,0.0001531274,0.0002402877,0.001134702,0.0005876592,0.001764914,0.0008947162,0.0009895741,0.0004059608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004578391,"about_ca_system_score_gemma":0.00037463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00144289,"about_ca_topic_score_gemma":0.003817695,"domain_scores_codex":[0.9985389,0.0007195761,0.00007862197,0.0001725126,0.0003646558,0.0001258564],"domain_scores_gemma":[0.9888208,0.007567277,0.001489831,0.0005059486,0.001180162,0.0004358789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000991669,0.002283184,0.7048038,0.00109886,0.0001321275,0.002421416,0.1200169,0.001462522,0.03462532,0.001825563,0.0199302,0.1104086],"study_design_scores_gemma":[0.00005602973,0.0008422988,0.8280163,0.0002708265,0.00007174286,0.00117563,0.1007704,0.02077065,0.009388618,0.002559609,0.03592911,0.0001487695],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950973,0.00008369719,0.002041186,0.00019393,0.00001499249,0.00009175503,0.001252935,0.0001004692,0.001123733],"genre_scores_gemma":[0.9894432,0.0001378668,0.005780547,0.0002489325,0.00003886409,0.0003005265,0.002822183,0.00007215011,0.001155742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001716481,"threshold_uncertainty_score":0.009077728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03968515645815301,"score_gpt":0.3028767562267842,"score_spread":0.2631915997686312,"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."}}