{"id":"W3084513432","doi":"10.1007/978-981-15-8731-3_5","title":"Emotion Detection on Twitter Textual Data","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Sadness; Sentiment analysis; Computer science; Social media; Anger; Feeling; Emotion detection; Information retrieval; Data science; World Wide Web; Natural language processing; Artificial intelligence; Psychology; Social psychology; Emotion recognition","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":[0.0005160877,0.0006280444,0.0004062618,0.002372369,0.0004321492,0.001230624,0.0003139392,0.0004795927,0.003227245],"category_scores_gemma":[0.002324262,0.0001403923,0.0004460819,0.001801771,0.0001360359,0.001255197,0.000498982,0.0005494226,0.004907815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003746545,"about_ca_system_score_gemma":0.0002307407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001542188,"about_ca_topic_score_gemma":0.002854611,"domain_scores_codex":[0.9995739,0.0000758211,0.00003695126,0.0000791311,0.0001759734,0.00005826526],"domain_scores_gemma":[0.9990929,0.0003848381,0.0001008543,0.00006717257,0.0003060879,0.00004814096],"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":[0.0005536644,0.0002523702,0.01859525,0.0006054532,0.0001075374,0.0005056187,0.0004126704,0.003979556,0.09183254,0.00259914,0.0890698,0.7914864],"study_design_scores_gemma":[0.00005382874,0.0004212913,0.07761528,0.0003003008,0.0002320721,0.001516631,0.00222421,0.6498327,0.1355569,0.009923255,0.122163,0.0001605352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5361586,0.006105436,0.3298525,0.004230542,0.002775503,0.0008849134,0.05700454,0.01416388,0.04882408],"genre_scores_gemma":[0.7371638,0.002840429,0.1651405,0.0005222171,0.001635726,0.0006242536,0.05659823,0.0005379443,0.03493695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003227245,"threshold_uncertainty_score":0.01079619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06260205722995042,"score_gpt":0.301748586922208,"score_spread":0.2391465296922576,"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."}}