{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00186242,0.0003056454,0.0003185503,0.002447395,0.0005719586,0.001170563,0.0002663646,0.0004445043,0.0008653112],"category_scores_gemma":[0.01045666,0.0001967855,0.0002941021,0.001997871,0.0002943778,0.001746108,0.0006266757,0.0004306858,0.0007161443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002898357,"about_ca_system_score_gemma":0.0002514715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001580728,"about_ca_topic_score_gemma":0.004615201,"domain_scores_codex":[0.9992937,0.0002719741,0.00007144721,0.0001562841,0.0001457865,0.00006075506],"domain_scores_gemma":[0.9916235,0.005217234,0.001181049,0.0005845522,0.0009876405,0.0004060587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001408523,0.0003997863,0.315066,0.0008781751,0.0002017541,0.0006185127,0.005149078,0.0008275609,0.09614784,0.001083291,0.009221466,0.5689979],"study_design_scores_gemma":[0.00009679644,0.0005849194,0.8737613,0.0001869204,0.000362185,0.001479911,0.005597457,0.06424501,0.02825536,0.005563745,0.01975016,0.0001162846],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.968734,0.0009355103,0.02166048,0.00026406,0.0001667745,0.0001065386,0.002234294,0.0004986398,0.00539972],"genre_scores_gemma":[0.9466582,0.0005148056,0.04699646,0.0001501705,0.0001486602,0.00009642361,0.003223143,0.0001207637,0.002091431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002447395,"threshold_uncertainty_score":0.009849548,"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."}}