{"id":"W2583888098","doi":"10.1109/icdmw.2016.0139","title":"Multi-sentiment Modeling with Scalable Systematic Labeled Data Generation via Word2Vec Clustering","year":2016,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada)","funders":"","keywords":"Word2vec; Computer science; Sentiment analysis; Scalability; Cluster analysis; Emoji; Social media; Classifier (UML); Binary classification; Artificial intelligence; Machine learning; Big data; Data mining; Data science; World Wide Web; Support vector machine","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":[0.0006073824,0.0001484742,0.0002499893,0.0001130017,0.0001481865,0.0002918465,0.0009068301,0.00003108034,0.00005690777],"category_scores_gemma":[0.00001576136,0.00008124496,0.00003421609,0.0002637566,0.00001043098,0.001086512,0.0005581366,0.00003327596,0.0001228361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004549987,"about_ca_system_score_gemma":0.00002470599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003739244,"about_ca_topic_score_gemma":0.0001044375,"domain_scores_codex":[0.9983444,0.00007497724,0.0003896885,0.0005773179,0.0003632251,0.0002504219],"domain_scores_gemma":[0.9983614,0.0000378253,0.0001247628,0.001312612,0.0000863957,0.00007697053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006695178,0.001443096,0.00564874,0.005057863,0.002845056,0.00008363763,0.00312484,0.2695882,0.6490027,0.01254847,0.00345891,0.04713152],"study_design_scores_gemma":[0.000529782,0.0000189275,0.0000053996,0.0006298422,0.0000320628,0.000005319529,0.00002526462,0.996585,0.001991062,0.0000063353,0.000009597118,0.0001613872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007467756,0.0001021086,0.991469,0.0003311475,0.0001462948,0.0002651541,7.357426e-7,0.0001162744,0.000101531],"genre_scores_gemma":[0.6086677,0.00001123248,0.3895137,0.0000918298,0.00006467348,0.00002149788,0.00001187058,0.00001075716,0.00160677],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7269968,"threshold_uncertainty_score":0.3313073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09569589603432707,"score_gpt":0.2842106160174,"score_spread":0.1885147199830729,"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."}}