{"id":"W55067354","doi":"","title":"INFORMATIONAL SUPPORT OR EMOTIONAL SUPPORT: PRELIMINARY STUDY OF AN AUTOMATED APPROACH TO ANALYZE ONLINE SUPPORT COMMUNITY CONTENTS","year":2010,"lang":"en","type":"article","venue":"International Conference on Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Emotional support; Content analysis; Support vector machine; Qualitative analysis; Online community; Machine learning; Data mining; Qualitative research; World Wide Web; Social support; Psychology","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":[0.003134034,0.0004128192,0.0003769446,0.00228643,0.000725992,0.0016132,0.0007720804,0.0006699245,0.001642766],"category_scores_gemma":[0.01805622,0.0002108503,0.0002956717,0.001367214,0.0005326777,0.001956335,0.000719748,0.0006411056,0.0007069272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006209721,"about_ca_system_score_gemma":0.0008284793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003403167,"about_ca_topic_score_gemma":0.003998494,"domain_scores_codex":[0.9972075,0.001425345,0.0001475225,0.0003422479,0.0007296228,0.0001477093],"domain_scores_gemma":[0.9750293,0.01915392,0.001476324,0.000992273,0.002929406,0.0004188574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001505893,0.004103226,0.153048,0.0012471,0.0001470853,0.0004327256,0.01227572,0.007165699,0.06634658,0.003961954,0.003024153,0.7467419],"study_design_scores_gemma":[0.0002411236,0.00293474,0.2871746,0.0001870855,0.0002100574,0.0009032816,0.01370238,0.6159403,0.05765341,0.008668812,0.01219947,0.0001848294],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.867354,0.0001682128,0.1236716,0.0003994196,0.00004039144,0.001329217,0.0006436692,0.0008792554,0.005514199],"genre_scores_gemma":[0.849245,0.0001195631,0.1476321,0.00008021341,0.00005796203,0.000637856,0.0005994046,0.00005683385,0.001571105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003403167,"threshold_uncertainty_score":0.0165745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0816712906517177,"score_gpt":0.3504108192172091,"score_spread":0.2687395285654914,"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."}}