{"id":"W3088709138","doi":"10.1109/compe49325.2020.9200054","title":"Long Short Term Memory (LSTM) based Deep Learning for Sentiment Analysis of English and Spanish Data","year":2020,"lang":"en","type":"article","venue":"2020 International Conference on Computational Performance Evaluation (ComPE)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Computer science; Dropout (neural networks); Deep learning; Artificial intelligence; Sentiment analysis; Recurrent neural network; Long short term memory; Regularization (linguistics); Term (time); Machine learning; Artificial neural network; Field (mathematics); Domain (mathematical analysis); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007535115,0.0006402773,0.0003122566,0.0003940199,0.0002230698,0.0004103063,0.0004305033,0.0003817007,0.001797848],"category_scores_gemma":[0.001599579,0.0001177171,0.0004551536,0.0004600043,0.0001360638,0.0007775883,0.0004155471,0.0008756867,0.0009671712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003233196,"about_ca_system_score_gemma":0.0003904655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004058455,"about_ca_topic_score_gemma":0.006637786,"domain_scores_codex":[0.9997571,0.00007691728,0.00001898573,0.00004878731,0.00005553654,0.00004270758],"domain_scores_gemma":[0.9996595,0.0001078842,0.00003557108,0.00002937063,0.0001501583,0.00001753633],"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":[0.0006744111,0.0004721908,0.006484908,0.0003993488,0.000230516,0.0003294527,0.0003641043,0.05866086,0.0687452,0.003028702,0.01814315,0.8424672],"study_design_scores_gemma":[0.00001719315,0.0001439446,0.002875167,0.00002458586,0.00004916716,0.00003387837,0.0001240605,0.9733809,0.01686141,0.002791665,0.00368262,0.00001539066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4564558,0.004022885,0.5175968,0.001468403,0.0007309256,0.0002144334,0.002348666,0.005122073,0.01204004],"genre_scores_gemma":[0.8697208,0.001043709,0.1188729,0.0002826723,0.000133239,0.0001065413,0.003420269,0.0001437258,0.006276161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004058455,"threshold_uncertainty_score":0.008069634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1162050507030065,"score_gpt":0.3522960819755168,"score_spread":0.2360910312725104,"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."}}