ml-code-generator / datasets /sentiment_analysis.csv
Surbhi
Feature extraction and model training
cedd211
review_text,sentiment_score,sentiment_label
Neutral,0.46,0.92
Good,0.3,0.16
Good,0.49,0.2
Good,0.99,0.86
Neutral,1.0,0.07
Bad,0.7,0.18
Bad,0.14,0.84
Bad,0.55,0.1
Good,0.84,0.17
Good,0.43,0.05
Bad,0.48,0.1
Bad,0.57,0.27
Neutral,0.3,0.1
Bad,0.05,0.25
Neutral,0.8,0.27
Neutral,0.12,0.29
Neutral,0.18,0.74
Good,0.29,0.85
Neutral,0.95,0.0
Neutral,0.13,0.41
Good,0.08,0.04
Good,0.76,0.16
Good,0.26,0.59
Good,0.59,0.18
Neutral,0.03,0.55
Neutral,0.48,0.76
Neutral,0.85,0.41
Good,0.01,0.85
Neutral,0.38,0.97
Good,0.94,0.88
Bad,0.23,0.49
Neutral,0.73,0.09
Good,0.78,0.31
Good,0.86,0.83
Good,0.71,0.2
Bad,0.75,0.32
Good,0.21,0.79
Bad,0.65,0.39
Bad,0.14,0.71
Bad,0.81,0.31
Good,0.36,0.61
Bad,0.18,0.69
Bad,0.58,0.61
Neutral,0.91,0.15
Bad,0.21,0.99
Good,0.3,0.98
Neutral,0.81,0.87
Bad,0.79,0.69
Neutral,0.83,0.71
Bad,0.09,0.82
Good,0.87,0.91
Neutral,0.4,0.86
Good,0.82,0.43
Good,0.81,0.94
Bad,0.09,0.3
Neutral,0.43,0.16
Neutral,0.23,0.71
Neutral,0.87,0.45
Good,0.93,0.45
Bad,0.34,0.85
Good,0.9,0.11
Bad,0.21,0.77
Good,0.11,0.13
Bad,0.85,0.01
Neutral,0.78,0.43
Good,0.19,0.7
Neutral,0.03,0.17
Good,0.67,0.48
Bad,0.09,0.49
Good,0.93,0.48
Neutral,0.56,0.65
Good,0.26,0.35
Good,0.51,0.61
Neutral,0.05,0.36
Bad,0.14,0.25
Neutral,0.39,0.93
Neutral,0.44,0.24
Good,0.72,0.79
Bad,0.72,0.58
Good,0.33,0.87
Neutral,0.76,0.81
Good,0.83,0.13
Bad,0.38,0.01
Neutral,0.63,0.52
Good,0.6,0.83
Good,0.76,0.33
Neutral,0.49,0.29
Neutral,0.82,0.39
Bad,0.03,0.27
Bad,0.95,0.67
Good,0.43,0.41
Bad,0.91,0.13
Bad,0.58,0.34
Bad,0.21,0.7
Good,0.92,0.86
Good,0.28,0.99
Bad,0.16,0.39
Neutral,0.8,0.44
Neutral,0.63,0.67
Neutral,0.48,0.77