{"id":"W3158053678","doi":"10.1007/978-3-030-73200-4_47","title":"LSTM Based Sentiment Analysis for Cryptocurrency Prediction","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":90,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Sentiment analysis; Social media; Cryptocurrency; Pipeline (software); Big data; Artificial intelligence; Precision and recall; Volume (thermodynamics); Social media analytics; Analytics; Data science; Natural language processing; Machine learning; Data mining; World Wide Web","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009397755,0.0005151196,0.00101089,0.003087079,0.0003733831,0.0008532507,0.002233539,0.0003576497,0.0006781964],"category_scores_gemma":[0.005454663,0.0004260315,0.0007231926,0.00344786,0.0005541763,0.0002597872,0.0006330155,0.0005403609,0.00002136254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003881962,"about_ca_system_score_gemma":0.0008486754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009335289,"about_ca_topic_score_gemma":0.00007817371,"domain_scores_codex":[0.9922428,0.0002079641,0.001247533,0.002458196,0.003215846,0.0006277337],"domain_scores_gemma":[0.9877939,0.00813995,0.0007115374,0.001861177,0.001281554,0.0002118559],"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":[0.00002518336,0.00003722823,0.002922123,0.00001873692,0.00008458037,0.00001576631,0.0001528743,0.1549251,0.00005830459,0.0008932056,0.0001732646,0.8406936],"study_design_scores_gemma":[0.0003110639,0.0001489721,0.002388543,0.0001643775,0.000185834,0.00000783433,3.800318e-7,0.8629223,0.0007812763,0.126648,0.005985313,0.0004561539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001454997,0.0002823731,0.9918255,0.0004151489,0.003369217,0.0006527263,0.00008660231,0.00007333763,0.003149639],"genre_scores_gemma":[0.03570705,0.000005580791,0.9599285,0.0007342248,0.0008233364,0.00005554585,0.0000459956,0.00004499758,0.002654756],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8402374,"threshold_uncertainty_score":0.9998192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08994810533514011,"score_gpt":0.3737289783395337,"score_spread":0.2837808730043936,"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."}}