media2

label_dicts.py

@@ -1,3 +1,89 @@
+CAP_MEDIA2_LABEL_NAMES = {
+    1: 'Macroeconomics',
+    2: 'Civil Rights',
+    3: 'Health',
+    4: 'Agriculture',
+    5: 'Labor',
+    6: 'Education',
+    7: 'Environment',
+    8: 'Energy',
+    9: 'Immigration',
+    10: 'Transportation',
+    12: 'Law and Crime',
+    13: 'Social Welfare',
+    14: 'Housing',
+    15: 'Banking, Finance, and Domestic Commerce',
+    16: 'Defense',
+    17: 'Technology',
+    18: 'Foreign Trade',
+    19: 'International Affairs',
+    20: 'Government Operations',
+    21: 'Public Lands',
+    23: 'Culture',
+    24: 'State and Local Government Administration',
+    25: 'Weather',
+    26: 'Fires, emergencies and natural disasters',
+    27: 'Crime and trials',
+    28: 'Arts, culture, entertainment and history',
+    29: 'Style and fashion',
+    30: 'Food',
+    31: 'Travel',
+    32: 'Wellbeing and learning',
+    33: 'Personal finance and real estate',
+    34: 'Personal technology and popular science',
+    35: 'Churches and Religion',
+    36: 'Celebrities and human interest',
+    37: 'Obituaries and death notices',
+    38: 'Sports',
+    39: 'Crosswords, puzzles, comics',
+    40: 'Media production/internal, letters',
+    41: 'Advertisements',
+    998: 'No Policy and No Media Content'
+}
+
+CAP_MEDIA2_NUM_DICT = {
+    0: 1,
+    1: 2,
+    2: 3,
+    3: 4,
+    4: 5,
+    5: 6,
+    6: 7,
+    7: 8,
+    8: 9,
+    9: 10,
+    10: 12,
+    11: 13,
+    12: 14,
+    13: 15,
+    14: 16,
+    15: 17,
+    16: 18,
+    17: 19,
+    18: 20,
+    19: 21,
+    20: 23,
+    21: 24,
+    22: 25,
+    23: 26,
+    24: 27,
+    25: 28,
+    26: 29,
+    27: 30,
+    28: 31,
+    29: 32,
+    30: 33,
+    31: 34,
+    32: 35,
+    33: 36,
+    34: 37,
+    35: 38,
+    36: 39,
+    37: 40,
+    38: 41,
+    39: 998
+}
+
 CAP_NUM_DICT = {
     0: 1,
     1: 2,