{"id":"W3036568251","doi":"10.1016/j.inffus.2020.06.002","title":"Deep learning based emotion analysis of microblog texts","year":2020,"lang":"en","type":"article","venue":"Information Fusion","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":149,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"National Key Research and Development Program of China; Natural Science Foundation of Shandong Province; Key Technology Research and Development Program of Shandong; National Natural Science Foundation of China","keywords":"Word2vec; Computer science; Microblogging; Artificial intelligence; Sentiment analysis; Convolutional neural network; Natural language processing; Support vector machine; Word (group theory); Feature (linguistics); Social media; Convolution (computer science); Feature vector; Artificial neural network; Pattern recognition (psychology); World Wide Web; Mathematics","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":[0.0001854704,0.00006430905,0.000147022,0.0004001243,0.00008446632,0.00008600258,0.0002210252,0.00003627801,0.0001817557],"category_scores_gemma":[0.00005439622,0.00005970121,0.0001353146,0.001699153,0.0000085415,0.0009127874,0.00008042814,0.00006156714,0.00008485704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001232576,"about_ca_system_score_gemma":0.00001339416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007705659,"about_ca_topic_score_gemma":9.58509e-7,"domain_scores_codex":[0.999177,0.00003918668,0.0003582749,0.00009346853,0.0002464846,0.00008561642],"domain_scores_gemma":[0.9993724,0.00003325086,0.0002900499,0.0001334471,0.0001207039,0.00005010008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001764239,0.00003531044,0.01398885,0.0000392088,0.0002908965,6.045445e-7,0.0104259,0.7254664,0.00610406,0.001813806,0.0005073811,0.2413099],"study_design_scores_gemma":[0.0001711741,0.00003412477,0.01133508,0.00000599213,0.00007005898,9.509193e-8,0.0001227383,0.9827091,0.002111006,0.00000505126,0.003371659,0.00006394894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05189547,0.00001684182,0.9462728,0.0006574956,0.00004744712,0.00004514184,6.115248e-7,0.00006484375,0.0009993428],"genre_scores_gemma":[0.9885672,0.00000793666,0.01059542,0.0006882036,0.00001217864,0.000001037186,0.0001170638,0.000001413442,0.00000954569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9366717,"threshold_uncertainty_score":0.2434545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01235506478018438,"score_gpt":0.2257955153964658,"score_spread":0.2134404506162814,"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."}}