{"id":"W2576441922","doi":"10.21700/ijcis.2016.118","title":"Sentiment Analysis of Arabic Tweets Using Semantic Resources","year":2016,"lang":"en","type":"article","venue":"International Journal of Computing and Information Sciences","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Arabic; Sentiment analysis; Natural language processing; Computer science; Artificial intelligence; Information retrieval; Linguistics; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005602196,0.0005066369,0.0002845633,0.002733602,0.000754195,0.001062484,0.0001493087,0.0002504049,0.002694792],"category_scores_gemma":[0.00206932,0.00007849648,0.0004702418,0.002036936,0.0001901716,0.0009989662,0.00045605,0.0003915936,0.001203592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004217811,"about_ca_system_score_gemma":0.0004125068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001298127,"about_ca_topic_score_gemma":0.001620792,"domain_scores_codex":[0.9995747,0.000117687,0.00005384736,0.00004856646,0.0001463384,0.00005881556],"domain_scores_gemma":[0.9990888,0.00030905,0.000101745,0.00003068923,0.0004314496,0.00003828315],"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":[0.003102006,0.0006267759,0.05821964,0.002054532,0.0003587371,0.001763508,0.005184977,0.004411695,0.2004785,0.0113543,0.04428601,0.6681592],"study_design_scores_gemma":[0.0001896548,0.001329836,0.2310033,0.0007823757,0.001094065,0.002597524,0.02598192,0.3468649,0.1919277,0.01879315,0.1791201,0.0003154837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9075865,0.001059617,0.05183034,0.001268987,0.0006434192,0.0004777969,0.009548723,0.00106795,0.02651668],"genre_scores_gemma":[0.9484252,0.000576121,0.03796107,0.0001217558,0.0002550107,0.000232827,0.007602361,0.00008423818,0.00474133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002733602,"threshold_uncertainty_score":0.009014964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02374293854566837,"score_gpt":0.3167267670036387,"score_spread":0.2929838284579703,"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."}}