{"id":"W1667860197","doi":"10.1109/icacci.2015.7275714","title":"Twitter sentiment classification using machine learning techniques for stock markets","year":2015,"lang":"en","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Bigram; Computer science; Artificial intelligence; tf–idf; Sentiment analysis; Machine learning; Weighting; Classifier (UML); Support vector machine; Term (time); Logistic regression; Natural language processing; Trigram","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0138772,0.0001687498,0.0002640527,0.00034339,0.000179644,0.0002467043,0.0004484514,0.00009770057,0.0003048293],"category_scores_gemma":[0.00979523,0.0001228425,0.0001185901,0.0005129889,0.00005564433,0.000250477,0.0001834987,0.000152658,0.00004100788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000146825,"about_ca_system_score_gemma":0.00008678916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003978055,"about_ca_topic_score_gemma":0.000006218942,"domain_scores_codex":[0.9967702,0.0007224606,0.0006221062,0.0005418045,0.00104434,0.0002990841],"domain_scores_gemma":[0.9963495,0.00201469,0.0003446406,0.0004918023,0.0006330764,0.0001663662],"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":[0.000709719,0.0001692785,0.1421801,0.00001763645,0.00005795749,0.000004443359,0.00103878,0.0001900822,0.03411956,0.0009425011,0.07151188,0.7490581],"study_design_scores_gemma":[0.0006140937,0.0001951809,0.007999538,0.0000267675,0.00003173722,0.00003564264,0.0006430012,0.7364888,0.02004819,0.01867621,0.2148686,0.0003722716],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09766274,0.00003856068,0.8770727,0.0006274627,0.0003858366,0.0006533638,0.000003195077,0.0002242982,0.02333185],"genre_scores_gemma":[0.238005,8.376035e-7,0.7376192,0.0002147512,0.0001509809,0.00006144991,0.000006006111,0.00002924962,0.02391254],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7486858,"threshold_uncertainty_score":0.9985457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3920788994815187,"score_gpt":0.4776138233793106,"score_spread":0.08553492389779188,"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."}}