{"id":"W2266481775","doi":"10.1007/978-3-319-25252-0_19","title":"Tweets as a Vote: Exploring Political Sentiments on Twitter for Opinion Mining","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Sentiment analysis; Process (computing); Suspect; Data science; Order (exchange); Set (abstract data type); Domain (mathematical analysis); Aggregate (composite); Information retrieval; Artificial intelligence; Political 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":[0.001155454,0.0006030715,0.0006531465,0.001159706,0.0002873548,0.0007452127,0.002284678,0.0002566657,0.00001911235],"category_scores_gemma":[0.0001432005,0.0005550999,0.0002674987,0.0004860749,0.0002668334,0.0007002372,0.001066775,0.0004668768,0.00009061396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004459087,"about_ca_system_score_gemma":0.0004201931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001643016,"about_ca_topic_score_gemma":0.000004431447,"domain_scores_codex":[0.9948885,0.00003999968,0.0006568375,0.001832832,0.001498117,0.001083749],"domain_scores_gemma":[0.9972288,0.0005642349,0.0002969241,0.001189798,0.0003190443,0.0004012016],"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":[0.0000776982,0.0002247814,0.0005283325,0.0001382436,0.0001914946,0.0001368289,0.004038581,0.02286535,0.000201816,0.2153518,0.002063583,0.7541814],"study_design_scores_gemma":[0.001745364,0.001239325,0.0001365088,0.002008678,0.00004675084,0.00006976724,0.000005330772,0.8034341,0.003694877,0.1577298,0.02776186,0.002127602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005280433,0.0002170509,0.988736,0.001593713,0.004152701,0.0004361868,0.000003833456,0.0001278442,0.004204654],"genre_scores_gemma":[0.3053257,0.00003764949,0.6792685,0.007703977,0.004615106,0.0001334279,0.0000702161,0.0001684285,0.002676964],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7805687,"threshold_uncertainty_score":0.9996901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1144324573528482,"score_gpt":0.3306665289324541,"score_spread":0.2162340715796059,"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."}}