When Collective Knowledge Meets Crowd Knowledge in a Smart City: A Prediction Method Combining Open Data Keyword Analysis and Case-Based Reasoning
Table 2
Association level for each word.
Word
Normal depression
Serious depression
Temperature
0.0005
0.0140
Humidity
0.0060
0.0065
Noise
0.0003
0.0108
Illumination
0.0034
0.0022
Anger
0.0350
0.0006
Stress
0.0277
0.0455
Fatigue
0.0309
0.0329
Fat
0.0064
0.1097
Diabetes
0.0148
0.3164
Heart disease
0.0001
0.0001
Hyperlipidemia
0.0014
0.0005
Cancer
0.0195
0.0002
Smoking
0.0032
0.0169
Drinking
0.0246
0.0641
Indigestion
0.0002
0.0001
Insomnia
0.0033
0.0169
Cold
0.0268
0.0002
Allergy
0.0005
0.1639
Neurosis
0.0000
0.0000
Blood pressure
0.0006
0.1176
Blood sugar
0.0060
0.0162
Wellness
0.0280
0.0329
Age
0.0241
0.0003
Gender
0.0089
0.0070
Weight
0.0324
0.0120
Solitude
0.0039
0.0054
Hobby
0.0156
0.0001
Yellow dust
0.0006
0.0000
Inconvenience
0.0024
0.0086
Note. Values in boldface indicate that the corresponding associated word is found more frequently. The meaning of these associated words helps to determine the depression level (normal or serious). For example, the high value for the associated word noise indicates that the depression level is likely to be more serious than normal.
