{"id":"W2542179328","doi":"10.1007/978-3-319-48674-1_55","title":"An Empirical Study and Comparison for Tweet Sentiment Analysis","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions","keywords":"Computer science; Sentiment analysis; Artificial intelligence; Support vector machine; Random forest; Machine learning; Feature selection; Domain (mathematical analysis); Selection (genetic algorithm); Empirical research; Feature (linguistics); Deep learning; Natural language processing","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":[0.001362238,0.0004747255,0.0009400793,0.001576417,0.0003320608,0.0008474173,0.002011872,0.0001763738,0.00002742332],"category_scores_gemma":[0.00002570203,0.0003627199,0.0002499349,0.0009246777,0.0002967309,0.0005160396,0.0007926253,0.0002602409,0.000009852976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001403257,"about_ca_system_score_gemma":0.0001432604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008301096,"about_ca_topic_score_gemma":0.00009321384,"domain_scores_codex":[0.9957798,0.00006706143,0.0006958956,0.001973704,0.0009401309,0.0005434644],"domain_scores_gemma":[0.9972879,0.0005032595,0.0003632539,0.001385553,0.0002183536,0.0002416848],"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":[0.00002489133,0.0005073362,0.0877225,0.00002524446,0.0008062269,0.00003394039,0.005197462,0.01504123,0.0001048628,0.004887223,0.00008489048,0.8855642],"study_design_scores_gemma":[0.0005881615,0.0006666651,0.004235078,0.0000631145,0.0002724946,0.000003565665,0.000002390281,0.9804653,0.0002163332,0.01214845,0.0006844348,0.0006540079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003363261,0.0002022341,0.9945254,0.0005125794,0.0005195963,0.0005496819,0.000006036415,0.00008173448,0.0002395173],"genre_scores_gemma":[0.7886106,0.000008485523,0.2100396,0.0005617718,0.0003933024,0.00002160447,0.00001022951,0.00002579922,0.0003286387],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9654241,"threshold_uncertainty_score":0.9998825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03888278192669729,"score_gpt":0.3434988057895813,"score_spread":0.304616023862884,"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."}}