{"id":"W2911517153","doi":"10.3169/mta.9.262","title":"[Paper] Measuring Similarity between Brands using Social Media Content","year":2021,"lang":"en","type":"article","venue":"ITE Transactions on Media Technology and Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre de Géomatique du Québec","funders":"","keywords":"Content (measure theory); Similarity (geometry); Advertising; Social media; User-generated content; Computer science; Information retrieval; Mathematics; Business; Artificial intelligence; World Wide Web","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":[0.0007700004,0.0004641057,0.0003494376,0.00463154,0.0004111067,0.0011614,0.0004981136,0.0008499132,0.004340022],"category_scores_gemma":[0.005928705,0.0001468499,0.0003986016,0.003078226,0.0003231576,0.001897891,0.0004926883,0.0003001564,0.002140074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00032909,"about_ca_system_score_gemma":0.0002048705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001694256,"about_ca_topic_score_gemma":0.001767509,"domain_scores_codex":[0.9990614,0.0001705863,0.00009150972,0.0001831554,0.0004384184,0.00005501325],"domain_scores_gemma":[0.997151,0.0008021751,0.0005320037,0.000288162,0.001135809,0.00009091534],"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":[0.0008347232,0.0005702875,0.266811,0.0006694868,0.0007550487,0.0002738198,0.0009564418,0.004508819,0.0215436,0.007854478,0.01901356,0.6762088],"study_design_scores_gemma":[0.0001261102,0.001206204,0.6333781,0.000210202,0.0008182153,0.002136979,0.00211622,0.2373368,0.05116403,0.01956612,0.05169456,0.0002464274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7796769,0.001905908,0.1587642,0.001426616,0.0008304394,0.0007122206,0.006063685,0.001409494,0.04921051],"genre_scores_gemma":[0.9579433,0.0003709513,0.03471523,0.0002109684,0.0002889509,0.000097756,0.001783033,0.00004502344,0.004544826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00463154,"threshold_uncertainty_score":0.0145188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08837451358766958,"score_gpt":0.2780816088001882,"score_spread":0.1897070952125187,"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."}}