{"id":"W2799696170","doi":"10.5539/cis.v11n2p76","title":"Corpus Analysis and Annotation for Helpful Sentences in Product Reviews","year":2018,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"King Abdulaziz University","keywords":"Computer science; Helpfulness; Annotation; Natural language processing; Product (mathematics); Quality (philosophy); Information retrieval; Artificial intelligence; Scheme (mathematics); Resource (disambiguation); Task (project management); Rank (graph theory)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007953393,0.0008602848,0.0006820799,0.009224132,0.001905313,0.001436394,0.0008882026,0.0007502611,0.003676889],"category_scores_gemma":[0.04125257,0.0003803132,0.0006148917,0.005419754,0.0007581501,0.001273027,0.002153868,0.001259736,0.002017774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009593118,"about_ca_system_score_gemma":0.002453688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003006792,"about_ca_topic_score_gemma":0.006577183,"domain_scores_codex":[0.9867153,0.006277068,0.001812998,0.001983663,0.002919035,0.0002920575],"domain_scores_gemma":[0.9392449,0.02940109,0.004369681,0.003696527,0.02262065,0.0006672104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00109127,0.0004032796,0.01995951,0.008399609,0.0001881146,0.002338641,0.02193738,0.003426291,0.1528753,0.01182093,0.1240996,0.6534601],"study_design_scores_gemma":[0.0002405159,0.0006473112,0.1064372,0.002266095,0.0005405759,0.002953987,0.01294317,0.06418566,0.110616,0.01186921,0.6868696,0.000430677],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3778183,0.00734981,0.5089813,0.002110464,0.002241076,0.007822298,0.04644902,0.006543063,0.04068465],"genre_scores_gemma":[0.2472022,0.001632076,0.6613898,0.0003712162,0.0005685518,0.01218387,0.0651421,0.001103143,0.01040703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009224132,"threshold_uncertainty_score":0.04206204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.024982395979917,"score_gpt":0.2967413276958779,"score_spread":0.2717589317159609,"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."}}