{"id":"W2252057809","doi":"10.3115/v1/s14-2076","title":"NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews","year":2014,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":709,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Data science; Information retrieval; Natural language processing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003536773,0.003026963,0.001606505,0.004270817,0.002127359,0.003981858,0.002242172,0.002500417,0.004695818],"category_scores_gemma":[0.01393342,0.0008098276,0.001153728,0.003609419,0.0007488593,0.00191083,0.001868899,0.002064424,0.005079177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005323591,"about_ca_system_score_gemma":0.01122582,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4546265,"about_ca_topic_score_gemma":0.6655841,"domain_scores_codex":[0.9957733,0.0009586558,0.0002533856,0.0007716324,0.001821681,0.0004213677],"domain_scores_gemma":[0.9889686,0.001503795,0.0003586547,0.0008910807,0.007201266,0.001076586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001118547,0.000666943,0.02700036,0.001390457,0.0006286369,0.0006367431,0.0009957199,0.002509373,0.01772739,0.001459008,0.7739741,0.1718927],"study_design_scores_gemma":[0.001065998,0.00106536,0.2296575,0.0005602734,0.0006173124,0.001570084,0.003860732,0.1735165,0.03414017,0.003571359,0.5498543,0.0005203648],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3943127,0.01374359,0.09091078,0.009736367,0.00623482,0.005414748,0.3813159,0.05001418,0.04831687],"genre_scores_gemma":[0.2051328,0.002222277,0.1536907,0.001381523,0.0006996011,0.00113258,0.5669489,0.00288809,0.06590348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5453735,"threshold_uncertainty_score":0.9039606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01415576873543839,"score_gpt":0.2401774215005479,"score_spread":0.2260216527651095,"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."}}