{"id":"W4210730697","doi":"10.1109/icmla52953.2021.00283","title":"Evaluating Sentiments in Social Media Comments on Tax Transformation in India using Deep Learning","year":2021,"lang":"en","type":"article","venue":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Key (lock); Government (linguistics); Quarter (Canadian coin); Social media; Computer science; Goods and services; Corporate governance; Deep learning; Artificial intelligence; Variation (astronomy); Transformation (genetics); Period (music); Natural language processing; Political science; Advertising; Data science; Business; Economics; World Wide Web; Economy; Computer security; Linguistics; Geography; Finance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063754,0.0001889081,0.0002326979,0.0004651358,0.0003261539,0.000324636,0.0003211839,0.00008272255,0.0001710932],"category_scores_gemma":[0.00008692939,0.0002086002,0.00006657717,0.000648107,0.00002881412,0.0003514504,0.00008979368,0.0006248332,0.00003555498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000134524,"about_ca_system_score_gemma":0.00006529764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004569272,"about_ca_topic_score_gemma":0.00005313899,"domain_scores_codex":[0.9978912,0.0003180371,0.000478205,0.0005048097,0.0005661713,0.0002415598],"domain_scores_gemma":[0.9992073,0.0002052412,0.0002428298,0.0001356772,0.0001485449,0.00006040552],"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":[0.00008067655,0.0008382031,0.07773454,0.0000452209,0.0001909788,0.00003532605,0.009486591,0.07369022,0.01434889,0.182095,0.00003630383,0.641418],"study_design_scores_gemma":[0.001046497,0.00004608573,0.01040138,0.0001261752,0.00001127624,0.000004598347,0.0007018005,0.984566,0.0007189085,0.001013893,0.001130775,0.0002325616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8001958,0.000214886,0.1702186,0.004706565,0.0006422061,0.0006237607,0.00002400055,0.0001224331,0.02325178],"genre_scores_gemma":[0.9958307,0.0001633014,0.003186036,0.0001372691,0.0001350498,0.00006552002,0.0002163504,0.00001209403,0.0002537383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9108758,"threshold_uncertainty_score":0.850647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08807877699985492,"score_gpt":0.3898915717117282,"score_spread":0.3018127947118733,"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."}}