{"id":"W6967465700","doi":"10.5281/zenodo.10953122","title":"SENTIMENT ANALYSIS FOR CONSUMER BEHAVIOR PREDICTION","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Sentiment analysis; Lexicon; Consumer confidence index; Variety (cybernetics); Transparency (behavior); Product (mathematics); Consumer behaviour; Analytics; Focus (optics)","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":[0.002072942,0.001150628,0.0009230693,0.002344426,0.0004535103,0.001431375,0.0005326149,0.0007559792,0.008108397],"category_scores_gemma":[0.006340105,0.0003261471,0.001292727,0.002107796,0.0002622816,0.001157196,0.0006925195,0.001228713,0.005837121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007231175,"about_ca_system_score_gemma":0.0006112515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003365417,"about_ca_topic_score_gemma":0.002912329,"domain_scores_codex":[0.9987012,0.0004410499,0.0001204388,0.0002504447,0.0003862244,0.0001006202],"domain_scores_gemma":[0.9980214,0.0008597234,0.0003145918,0.0001481113,0.0005991174,0.00005707579],"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":[0.0005480755,0.0004484932,0.0325907,0.001030379,0.0004397908,0.0003729325,0.0004916305,0.02626065,0.01261916,0.01208484,0.0881502,0.8249632],"study_design_scores_gemma":[0.00006277471,0.0003595617,0.0416104,0.0004579927,0.0001944925,0.0003300163,0.0007685265,0.8384563,0.007924011,0.03742848,0.07226626,0.0001412729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1257683,0.01237844,0.7672121,0.005099784,0.002271738,0.001856329,0.02791145,0.009344095,0.04815779],"genre_scores_gemma":[0.6604857,0.00644726,0.2885704,0.001022271,0.001180399,0.001358166,0.02376785,0.0004519414,0.01671607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008108397,"threshold_uncertainty_score":0.0271253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04417418093545444,"score_gpt":0.2769941993016306,"score_spread":0.2328200183661761,"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."}}