{"id":"W3105333265","doi":"10.48550/arxiv.1906.02331","title":"OutdoorSent: Sentiment Analysis of Urban Outdoor Images by Using Semantic and Deep Features","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Sentiment analysis; Generalization; Context (archaeology); Artificial intelligence; Information retrieval; Data science; Machine learning; Geography","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":[0.0002926379,0.001277999,0.0003453636,0.001167381,0.000251751,0.0005940934,0.0004540704,0.0005011501,0.002683116],"category_scores_gemma":[0.0006366783,0.0001417636,0.0005547265,0.0007457102,0.0001538312,0.0007747347,0.0005042362,0.0004672093,0.001389983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004503083,"about_ca_system_score_gemma":0.0002707165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005090749,"about_ca_topic_score_gemma":0.01501416,"domain_scores_codex":[0.9998411,0.00002276172,0.000008812914,0.00004823787,0.00003604535,0.00004306935],"domain_scores_gemma":[0.9998446,0.00002944253,0.00003241237,0.00002016476,0.00005540174,0.00001793645],"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":[0.001260004,0.00108381,0.07269788,0.0007142585,0.0007065905,0.0005962471,0.0005548669,0.02472534,0.09457637,0.002232098,0.1042921,0.6965603],"study_design_scores_gemma":[0.0001219157,0.000704544,0.08652139,0.0000987683,0.0002698616,0.0003749685,0.001036477,0.8412598,0.03872752,0.004010004,0.02680587,0.00006893344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7920022,0.001623562,0.142526,0.00105815,0.0007033147,0.0006701718,0.02424612,0.01122106,0.02594942],"genre_scores_gemma":[0.8886929,0.0004132369,0.07085726,0.0003202021,0.0002275335,0.0002543253,0.02924811,0.0002290879,0.00975738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005090749,"threshold_uncertainty_score":0.01012224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02521259281786398,"score_gpt":0.1880397882316355,"score_spread":0.1628271954137716,"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."}}