{"id":"W3163550658","doi":"10.1016/j.neucom.2021.05.040","title":"MASAD: A large-scale dataset for multimodal aspect-based sentiment analysis","year":2021,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; York University","funders":"East China Normal University","keywords":"Sentiment analysis; Computer science; Task (project management); Artificial intelligence; Scale (ratio); Mainstream; Natural language processing; Information retrieval; Machine learning","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":[0.0006519265,0.001136958,0.0005168976,0.00302899,0.0007370309,0.0009163525,0.0009432075,0.001076906,0.006629741],"category_scores_gemma":[0.003509212,0.0002273078,0.0008203674,0.002633869,0.0001952081,0.001039757,0.001176935,0.0009481207,0.006598502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006359475,"about_ca_system_score_gemma":0.001068786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009293117,"about_ca_topic_score_gemma":0.02632666,"domain_scores_codex":[0.9993103,0.0001262572,0.00008984879,0.0001279962,0.0002650595,0.00008062761],"domain_scores_gemma":[0.9987036,0.0002973445,0.0001456351,0.0002064377,0.0004672433,0.0001796945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009051803,0.0007209569,0.03067589,0.001968713,0.0004150119,0.0007224891,0.0006657766,0.003747788,0.02372047,0.00255261,0.8061156,0.1277895],"study_design_scores_gemma":[0.000530415,0.0005238442,0.1821181,0.000367773,0.0003723688,0.001213138,0.002045797,0.04838774,0.0188966,0.005814943,0.7394166,0.0003127056],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.07801478,0.0009129878,0.01099569,0.0007073242,0.0004635714,0.0007673953,0.8900657,0.008316416,0.00975617],"genre_scores_gemma":[0.06356147,0.0003749704,0.027094,0.0002766837,0.0001320329,0.001079309,0.90192,0.0002968473,0.005264681],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009293117,"threshold_uncertainty_score":0.02217865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01964842168906004,"score_gpt":0.2903483329775977,"score_spread":0.2706999112885377,"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."}}