{"id":"W2156413587","doi":"10.1613/jair.4272","title":"Sentiment Analysis of Short Informal Texts","year":2014,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":890,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; SemEval; Task (project management); Lexicon; Sentiment analysis; Phrase; Natural language processing; Artificial intelligence; Variety (cybernetics); Word (group theory); Term (time); Set (abstract data type); Linguistics","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":[0.0008472537,0.0008502281,0.0005542776,0.001855311,0.0003991889,0.001054762,0.0003945854,0.000349615,0.004946096],"category_scores_gemma":[0.00449419,0.0001653096,0.0005781113,0.001020459,0.0002226372,0.001014735,0.0006578276,0.0004129567,0.004366931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002938476,"about_ca_system_score_gemma":0.0003167928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006610026,"about_ca_topic_score_gemma":0.001277136,"domain_scores_codex":[0.9988631,0.0002494388,0.0001310711,0.0001997902,0.0004717226,0.00008483694],"domain_scores_gemma":[0.9974105,0.000837594,0.0003894728,0.0001982417,0.001066459,0.00009781727],"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":[0.0007428139,0.0002456992,0.01720615,0.001535601,0.0002159345,0.0006995876,0.001047214,0.003034159,0.2145821,0.003283647,0.04082642,0.7165807],"study_design_scores_gemma":[0.0002005396,0.001285425,0.1620207,0.0005289968,0.0003904366,0.002338798,0.002571456,0.3234358,0.2359157,0.02292166,0.2481312,0.0002593223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4365283,0.003157627,0.4843453,0.001237366,0.001075216,0.001594182,0.02267822,0.01328597,0.03609788],"genre_scores_gemma":[0.7146364,0.001647339,0.2279976,0.0004047088,0.001023147,0.001029716,0.03492114,0.0007610975,0.0175788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004946096,"threshold_uncertainty_score":0.01654631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1410959079151924,"score_gpt":0.4268329994839138,"score_spread":0.2857370915687214,"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."}}