{"id":"W2577073647","doi":"10.1109/ictai.2016.0069","title":"Improving Deep Belief Networks via Delta Rule for Sentiment Classification","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Deep belief network; Boltzmann machine; Computer science; Artificial intelligence; Restricted Boltzmann machine; Layer (electronics); Artificial neural network; Deep learning; Backpropagation; Sentiment analysis; Machine learning; Unsupervised learning; Pattern recognition (psychology); 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":[],"consensus_categories":[],"category_scores_codex":[0.0003199836,0.0001036577,0.0001161686,0.00007809624,0.0001492117,0.0001340704,0.0004109765,0.00004790058,0.00007882145],"category_scores_gemma":[0.0000142072,0.00006784036,0.0001101607,0.0001756473,0.00001527083,0.0003751072,0.000130046,0.00002638764,0.00005204271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004376588,"about_ca_system_score_gemma":0.00001446049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001290518,"about_ca_topic_score_gemma":0.000008731832,"domain_scores_codex":[0.9989137,0.00002310798,0.0002544023,0.0003785048,0.000166071,0.0002641948],"domain_scores_gemma":[0.9991925,0.0001080831,0.0001286338,0.000408541,0.00008839794,0.00007379937],"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":[0.000004809436,0.00005800326,0.00152255,0.000004163506,0.00004894328,3.982301e-7,0.00006326453,0.0001538194,0.01437814,0.03331691,0.001936215,0.9485128],"study_design_scores_gemma":[0.0003603299,0.00003286272,0.001203257,0.000009168543,0.00001340393,8.996882e-7,0.00001621989,0.9910374,0.004015097,0.0006326996,0.002538554,0.0001401064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001288577,0.00008081752,0.9962427,0.001281937,0.0003176657,0.0001499433,4.455098e-7,0.0001047062,0.0005332014],"genre_scores_gemma":[0.7919123,0.00002422545,0.2048647,0.0004360269,0.0002669099,0.00006135753,0.00001371756,0.00001325384,0.002407497],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9908836,"threshold_uncertainty_score":0.2766449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0200385010352133,"score_gpt":0.2538341298858324,"score_spread":0.2337956288506191,"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."}}