{"id":"W3107845597","doi":"10.1080/1206212x.2020.1851501","title":"A two-level deep learning approach for emotion recognition in Arabic news headlines","year":2020,"lang":"en","type":"article","venue":"International Journal of Computers and Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université TÉLUQ","funders":"Qatar National Library","keywords":"Computer science; Sadness; Sentiment analysis; Artificial intelligence; Disgust; Machine learning; Decision tree; Trigram; Naive Bayes classifier; Happiness; Convolutional neural network; Surprise; Random forest; Support vector machine; Anger; Natural language processing; Psychology; Social psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001791261,0.00007064667,0.000135902,0.0001754181,0.00005249296,0.0001624145,0.0003635481,0.00002163589,0.000002351877],"category_scores_gemma":[0.00002205839,0.00006601567,0.00008559632,0.0002004762,0.00001380537,0.0003255589,0.00005979262,0.000108609,0.000001725037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002145767,"about_ca_system_score_gemma":0.00001958446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004907358,"about_ca_topic_score_gemma":0.000001383748,"domain_scores_codex":[0.9991764,0.00002992556,0.0003678968,0.0001613417,0.000186263,0.00007816689],"domain_scores_gemma":[0.9992448,0.00008461886,0.0002814389,0.00004940313,0.0002720228,0.00006768237],"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":[0.00003813359,0.0001638263,0.003500415,0.00001531591,0.0001179751,0.000003029862,0.001183373,0.03861487,0.0006465908,0.01166335,0.0002613807,0.9437917],"study_design_scores_gemma":[0.001128152,0.00008349103,0.001840959,0.00003104574,0.00001263041,0.00003463346,0.0002286117,0.9900624,0.0001183952,0.002349223,0.004005237,0.0001052345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009524886,0.0001293872,0.9862962,0.003720113,0.0001110747,0.0001162004,0.00000173151,0.0000117271,0.00008862436],"genre_scores_gemma":[0.5744644,0.0001158005,0.4239881,0.0006656098,0.0007062536,0.0000186073,0.00002896268,0.000005102015,0.000007203025],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9514475,"threshold_uncertainty_score":0.2692041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06159624203719277,"score_gpt":0.3058940405224249,"score_spread":0.2442977984852321,"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."}}