{"id":"W2800743636","doi":"10.2139/ssrn.3067734","title":"The R Package sentometrics to Compute, Aggregate and Predict with Textual Sentiment","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke; Center for Interuniversity Research and Analysis on Organizations; HEC Montréal","funders":"","keywords":"Indexation; Computer science; Sentiment analysis; Aggregate (composite); R package; Workflow; Index (typography); Information retrieval; Natural language processing; Database; World Wide Web; Programming language; Economics; Monetary policy","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.004418742,0.00009406971,0.0001272518,0.0001179236,0.003944156,0.0007976125,0.000457822,0.00003109958,0.00000793667],"category_scores_gemma":[0.0003140935,0.00005801465,0.00005939437,0.0002903959,0.00022328,0.0001802575,0.00009386491,0.0005205122,0.00001062226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003004193,"about_ca_system_score_gemma":0.0009735456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000453202,"about_ca_topic_score_gemma":0.00763434,"domain_scores_codex":[0.9977893,0.0001876222,0.0001554927,0.00015697,0.0005974216,0.001113162],"domain_scores_gemma":[0.9989974,0.0002745561,0.0002166323,0.0001699065,0.0001629281,0.0001784975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000615044,0.00004570713,0.01910262,0.000001613795,0.0003891138,0.00001022729,0.001487927,0.0001085194,0.00001436816,0.352288,0.0003648629,0.6261256],"study_design_scores_gemma":[0.002697946,0.001398735,0.1492094,0.000102521,0.0004125561,0.0003709529,0.02381145,0.003030013,0.00006947229,0.6055241,0.2124802,0.0008926824],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7132931,0.003246805,0.2438518,0.02552818,0.0004205142,0.0003911793,0.000003714476,0.0000526935,0.01321199],"genre_scores_gemma":[0.9908342,0.0024601,0.001196594,0.00007459034,0.000452046,0.000002492667,6.592327e-7,0.000007782959,0.004971567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6252329,"threshold_uncertainty_score":0.9973526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01744146395906499,"score_gpt":0.334739107158963,"score_spread":0.3172976431998981,"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."}}