{"id":"W2406949971","doi":"","title":"All Blogs Are Not Made Equal: Exploring Genre Differences in Sentiment Tagging of Blogs.","year":2007,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Sentence; Annotation; Valence (chemistry); Negation; Subjectivity; Sentiment analysis; Natural language processing; German; Artificial intelligence; Information retrieval; Linguistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0007714604,0.0001570555,0.0003091513,0.0003691409,0.00004986125,0.00008059647,0.0005937288,0.00004621941,0.00008780121],"category_scores_gemma":[0.00001761415,0.0001329626,0.0001117373,0.0004867823,0.0000279613,0.0004019722,0.0003097671,0.0001085621,0.00002157044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003586322,"about_ca_system_score_gemma":0.00001263413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001443986,"about_ca_topic_score_gemma":0.00004768515,"domain_scores_codex":[0.9981757,0.00004309259,0.0005341964,0.0004045171,0.0004374099,0.0004050595],"domain_scores_gemma":[0.9991554,0.0001404829,0.0002156877,0.0003591014,0.00004272081,0.00008659298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003739573,0.0008365534,0.812362,0.0001169058,0.0003787833,0.0001747227,0.01341953,0.0006472837,0.04475246,0.06290847,0.000676316,0.06368954],"study_design_scores_gemma":[0.001650739,0.0001423834,0.5819424,0.000357799,0.00005870844,0.00000835955,0.006649818,0.1228029,0.2825933,0.0005402246,0.002243511,0.001009895],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.809549,0.0001073415,0.188191,0.000615837,0.0002895813,0.00008006363,4.349817e-7,0.00007261543,0.001094113],"genre_scores_gemma":[0.9834567,0.00004784554,0.01557887,0.0003771644,0.00004893735,0.000006111426,0.000001947635,0.000006521622,0.0004758947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2378409,"threshold_uncertainty_score":0.5422056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1348669508147793,"score_gpt":0.2983925222328046,"score_spread":0.1635255714180253,"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."}}