{"id":"W2162010436","doi":"","title":"Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon","year":2010,"lang":"en","type":"article","venue":"NPARC","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":873,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Lexicon; Computer science; Natural language processing; Word (group theory); Sentiment analysis; Emotion classification; Orientation (vector space); Artificial intelligence; Term (time); Polarity (international relations); Quality (philosophy); Emotion recognition; Semantics (computer science); Speech recognition; Linguistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0002961407,0.0001096917,0.0001565611,0.0001115267,0.0002051847,0.0002686348,0.0003074619,0.00006482094,0.0001537535],"category_scores_gemma":[0.00002343054,0.0001037657,0.00004574946,0.0003041209,0.00002625761,0.0004142801,0.0001512367,0.0001420539,0.00001745056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001596991,"about_ca_system_score_gemma":0.00001947452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004550653,"about_ca_topic_score_gemma":0.00004435993,"domain_scores_codex":[0.9989924,0.00006740085,0.000186741,0.0003532241,0.0002014534,0.000198714],"domain_scores_gemma":[0.9992895,0.00003785532,0.00005878595,0.0003925985,0.00003091199,0.0001903894],"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":[0.000007317531,0.0001418409,0.001566809,0.000004093636,0.00002504835,0.000003089903,0.001347864,0.0000537246,0.9327102,0.02860553,0.0007848089,0.03474968],"study_design_scores_gemma":[0.0006005432,0.0002306648,0.00295455,0.00004977722,0.00005031686,0.00002238123,0.0003592642,0.9389374,0.04147058,0.01335943,0.001520512,0.0004446217],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8342811,0.000005409045,0.1638248,0.0008447588,0.0002475816,0.000077498,0.000002529743,0.00006455239,0.0006517582],"genre_scores_gemma":[0.9330982,0.000003031941,0.06644311,0.0002280438,0.00008653854,0.000002623451,0.00001093878,0.000007214893,0.0001203281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9388836,"threshold_uncertainty_score":0.4231441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02472797718163747,"score_gpt":0.2964459804661465,"score_spread":0.271718003284509,"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."}}