{"id":"W4250951234","doi":"10.32920/ryerson.14645355","title":"Microblog summarization based on sentiment and aspect analysis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Automatic summarization; Microblogging; Sentiment analysis; Social media; Computer science; Information retrieval; Baseline (sea); Cluster analysis; Multi-document summarization; Annotation; Data science; Natural language processing; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002581755,0.0002758657,0.0004905445,0.0007929966,0.00007291132,0.000512609,0.0006324241,0.0001743278,0.0000862262],"category_scores_gemma":[0.00002822361,0.0002618404,0.0003173168,0.001210817,0.00003120329,0.0001444916,0.001280773,0.0002795656,0.000004400512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001256545,"about_ca_system_score_gemma":0.00008400597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000914314,"about_ca_topic_score_gemma":0.0001188177,"domain_scores_codex":[0.9979254,0.0001263875,0.0003238221,0.001084758,0.0003482976,0.0001913597],"domain_scores_gemma":[0.9979982,0.00008177711,0.0002150999,0.001470469,0.0001583635,0.0000760464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003281928,0.002069269,0.04868127,0.0004631747,0.01029514,0.000425824,0.001079622,0.6953448,0.01927586,0.08338958,0.003572193,0.1353704],"study_design_scores_gemma":[0.00009265813,0.00002489091,0.002646228,0.00004300747,0.0005021999,6.728733e-7,0.000006773422,0.9689038,0.02453663,0.002707529,0.0001620364,0.0003735948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002073155,0.00009783157,0.9935606,0.0006950179,0.00006007805,0.0001736273,0.000003147958,0.0004335965,0.002902983],"genre_scores_gemma":[0.4978348,0.00005601568,0.5009136,0.0005385749,0.00001725143,0.00002545814,0.0001713357,0.0000103286,0.0004326408],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4957616,"threshold_uncertainty_score":0.9999834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009684912507483769,"score_gpt":0.263804563741241,"score_spread":0.2541196512337572,"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."}}