{"id":"W2952865927","doi":"10.1109/iri.2016.66","title":"Rapid Prototyping of a Text Mining Application for Cryptocurrency Market Intelligence","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Seneca Polytechnic","funders":"","keywords":"Cryptocurrency; Sentiment analysis; Computer science; Analytics; Data science; Cloud computing; Blockchain; Pipeline (software); Big data; Formal concept analysis; Scale (ratio); Software; Social media; Software engineering; World Wide Web; Artificial intelligence; Computer security; Data mining; Operating system","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":[0.002163739,0.0009191901,0.0006438663,0.001836252,0.0005369692,0.00171396,0.001858192,0.0009919084,0.01515301],"category_scores_gemma":[0.005834491,0.0004826843,0.0005679985,0.0012397,0.0004004308,0.002899771,0.001036767,0.001323827,0.006139093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004664868,"about_ca_system_score_gemma":0.0008297232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001680109,"about_ca_topic_score_gemma":0.002504212,"domain_scores_codex":[0.9988831,0.0002299889,0.0001004968,0.0002390446,0.0004670381,0.00008042125],"domain_scores_gemma":[0.9965389,0.001989107,0.0001271589,0.0004199047,0.0006824276,0.0002426213],"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":[0.001709576,0.001440336,0.008523703,0.001479574,0.0004037728,0.003219044,0.001806743,0.02053784,0.134004,0.01538145,0.1160999,0.6953939],"study_design_scores_gemma":[0.0005217474,0.0005416451,0.006090442,0.000199414,0.00008904118,0.0009100885,0.0004561437,0.7626848,0.08812607,0.0159956,0.1242483,0.0001368147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08933003,0.0005471015,0.6911955,0.002477726,0.0006648914,0.00332037,0.007197423,0.1860908,0.01917611],"genre_scores_gemma":[0.2954158,0.000462735,0.6684449,0.0006168426,0.0001966307,0.001196403,0.008301011,0.00461662,0.02074907],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01515301,"threshold_uncertainty_score":0.0506919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03646573168083288,"score_gpt":0.2961540965920978,"score_spread":0.259688364911265,"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."}}