{"id":"W1981498954","doi":"10.1016/j.inffus.2014.04.002","title":"An approach to rank reviews by fusing and mining opinions based on review pertinence","year":2014,"lang":"en","type":"article","venue":"Information Fusion","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Francis Xavier University","funders":"Fundamental Research Funds for the Central Universities","keywords":"Automatic summarization; Relevance (law); Computer science; Information retrieval; Ranking (information retrieval); Similarity (geometry); Rank (graph theory); Sentiment analysis; Filter (signal processing); Metric (unit); Data science; Data mining; Artificial intelligence; Image (mathematics); Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001154538,0.0001197084,0.0001978575,0.0001558976,0.0002153806,0.0002142803,0.0003129684,0.00003158812,0.00001765035],"category_scores_gemma":[0.0001546388,0.00009621907,0.0000474985,0.0004350978,0.000009644528,0.001199757,0.00007258144,0.00005854174,0.00007054787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001516338,"about_ca_system_score_gemma":0.00001226909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005074203,"about_ca_topic_score_gemma":1.484963e-7,"domain_scores_codex":[0.9988192,0.0001174422,0.0004505161,0.0001899631,0.0002784136,0.0001445166],"domain_scores_gemma":[0.9991448,0.00004818297,0.0002019111,0.0004195542,0.00006203738,0.0001235065],"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":[0.000006872167,0.00006936253,0.0003763236,0.0004172436,0.000003581765,5.153371e-8,0.002044276,0.001643859,0.000411713,0.001726386,0.04473395,0.9485664],"study_design_scores_gemma":[0.0001611106,0.00006092185,0.0003987802,0.00074395,0.000005619419,0.0000010483,0.00002544702,0.7416515,0.00005757115,0.000003566914,0.2567605,0.0001299111],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002162441,0.0006559857,0.9890625,0.001711805,0.0001236362,0.0003492773,0.000001648508,0.00007380672,0.005858919],"genre_scores_gemma":[0.369021,0.00506576,0.5327581,0.09153587,0.0002418288,0.0001906497,0.001040632,0.00002522866,0.0001209074],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9484365,"threshold_uncertainty_score":0.39237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02001360541945994,"score_gpt":0.2723351873363451,"score_spread":0.2523215819168851,"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."}}