In [614]:
import pandas as pd
import nltk as nltk
from nltk.corpus import stopwords
import glob
import scipy as scipy
import collections

In [639]:
allFiles = glob.glob("*.txt")
df = pd.DataFrame()
#list = []
for files in allFiles:
    frame = pd.read_table(files)
    df = df.append(frame, ignore_index=True)

df = df.dropna()

In [640]:
df['tokens'] = df.Headline.apply(lambda x: nltk.word_tokenize(x.lower()))

In [641]:
df['tokens'] = df.tokens.apply(lambda x: [word.translate(None, "!@#$;':-*^&") for word in x])

In [461]:


In [642]:
replacements = [
                ("s'pore","singapore"),
                ("spore", "singapore"),
                ("govt", "government"),
                ("pts", "points"),
                ]

In [643]:
def version_replace(x, replacements):
    for item in replacements:
        if x == item[0]:
            x = item[1]
        else:
            x = x
    return x

In [644]:
english_stops = set(stopwords.words('english'))
english_stops.discard('up')
english_stops.discard('down')
english_stops.add('...')
english_stops.add("'s")
english_stops.add('mr.')
english_stops.add("n't")
english_stops.add("'")
english_stops.add(":")

In [645]:
df['tokens'] = df['tokens'].apply(lambda x: [word for word in x if word not in english_stops and len(word) > 1])
df['tokens'] = df['tokens'].apply(lambda x: [version_replace(word, replacements) for word in x])

In [532]:
df58 = df[df.year == '1958']

In [646]:
fd = nltk.FreqDist([word for sent in df['tokens'] for word in sent])

In [647]:
word = []
count = []
for num, i in enumerate(fd.keys()[:100]):
    word.append(i)
    count.append(fd.values()[num])
    
freqData = pd.DataFrame()
freqData['words'] = word
freqData['count'] = count

In [648]:
freqData


Out[648]:
words count
0 singapore 3637
1 new 3293
2 up 2481
3 two 1745
4 man 1560
5 us 1559
6 talks 1553
7 says 1547
8 big 1363
9 lee 1363
10 tengku 1357
11 pm 1326
12 government 1293
13 latest 1222
14 police 1184
15 chinese 1158
16 china 1117
17 killed 1117
18 may 1072
19 today 1008
20 back 1001
21 death 992
22 crash 979
23 nt 976
24 get 939
25 page 919
26 strike 909
27 plan 894
28 go 892
29 first 879
30 dies 852
31 year 850
32 column 831
33 men 818
34 miscellaneous 811
35 three 800
36 visit 800
37 gets 779
38 call 756
39 reds 753
40 fire 746
41 red 746
42 pay 741
43 held 740
44 day 737
45 die 722
46 home 719
47 malaysia 713
48 top 669
49 dead 666
50 down 655
51 found 650
52 world 627
53 war 620
54 girl 609
55 asean 605
56 hurt 605
57 bid 591
58 help 589
59 way 582
... ...

100 rows × 2 columns


In [610]:
df['year'] = df['Date'].apply(lambda x: str(x)[:4])

In [694]:
mylist = ['honoured','malaya']

def find_token(mylist, dflist):
    for i in dflist:
        for j in mylist:
            if i == j:
                return True
            else:
                pass
topdf = pd.DataFrame()
for word in freqData['words']:
    tempdf = df[df['tokens'].apply(lambda x: word in x)]
    topdf = topdf.append(tempdf)

In [696]:
len(topdf)
len(df)


Out[696]:
112017

In [592]:
'''
yearDict = dict()
for year in df.year:
    if year in yearDict:
        continue
    else:
        yearDict[year] = 0
'''

wordDict = dict()
for row in df.tokens:
    for word in row:
        if word in wordDict:
            continue
        else:
            wordDict[word] = dict()

In [513]:
for num, row in enumerate(df.tokens[:1]):
    for word in row:
        year = df.year[num]
        if word in wordDict:
            if year in wordDict[word]:
                print type(wordDict[word])
                print wordDict[word][year]


<type 'dict'>
0
<type 'dict'>
0
<type 'dict'>
0

In [593]:
for row in range(len(freqData)):
    word = freqData['words'][row]
    word_count = freqData['count'][row]
    wordDict[word]['1958'] = word_count

In [598]:
wordDict['man']


Out[598]:
{'1958': 76}

In [ ]:

Co-occurrence Matrix


In [611]:
def cooccurence_matrix_corpus(corpus):
    matrix = collections.defaultdict(lambda : collections.defaultdict(int))
    
    for corpora in corpus:
        for i in xrange(len(corpora)-1):
            for j in xrange(i+1, len(corpora)):
                word1, word2 = [corpora[i], corpora[j]]
                matrix[word1][word2] += 1
                matrix[word1][word2] += 1
    return matrix

In [615]:
ccm = cooccurence_matrix_corpus(df['tokens'])

In [616]:
ccm['index']


Out[616]:
defaultdict(<type 'int'>, {'45': 2, '36': 4, '1.156': 2, '1,800': 2, 'regional': 2, 'twoyear': 2, 'fall': 8, 'years': 6, 'leads': 2, '15.61': 2, '46point': 2, '3,000': 2, '7.5': 2, '2,200': 2, '12.9': 2, '32': 2, 'line': 4, 'move': 2, '700mark': 2, '10.1': 2, 'nearer': 2, 'buyers': 4, '10.12': 2, '20': 24, '21': 12, '22': 14, '23': 10, 'nervous': 2, '28': 14, '12.85': 2, 'fund': 2, 'good': 2, 'signals': 2, 'institutional': 2, 'breaches': 8, '16.3': 2, '400': 4, '33.41': 2, 'breach': 2, '25pt': 2, 'momentum': 2, 'lack': 2, 'return': 4, 'gains': 28, 'stay': 4, '10': 36, 'rules': 2, 'breaks': 14, 'continues': 8, '800level': 2, '26.68': 2, 'break': 2, 'next': 2, '12': 16, 'worst': 2, 'nearly': 6, '800pt': 4, 'sheds': 18, '25.38': 2, 'chips': 4, 'day': 6, 'cross': 2, '58': 4, '13.7': 2, 'loss': 2, '1400': 2, 'shattered': 2, '9.04': 2, 'level': 26, '55': 2, 'january': 2, 'drop': 4, '50': 2, '53': 2, 'slide': 6, '25': 4, 'bounces': 2, '54': 2, 'alltime': 24, 'stays': 2, 'dip': 2, 'heavy': 2, 'poor': 2, 'pane1': 2, 'interest': 2, '800mark': 4, '66': 2, '30point': 2, '18.79': 2, 'declines': 4, '1,044': 2, 'inching': 2, '700': 6, 'back': 2, 'slowdown': 2, 'past': 18, '2.9': 2, 'street': 6, 'trading': 14, 'year': 12, 'close': 4, 'news': 2, 'rebounds': 2, 'dives': 2, 'profittaking': 4, '84': 2, 'blue': 4, 'closes': 6, '31.34': 2, '10.67': 2, 'revised': 2, '792': 2, 'rate': 2, 'fourth': 2, '900level': 2, '13.52': 2, 'since': 4, '1,100pt': 2, 'looking': 2, '27': 8, '1,976.09': 2, '17pt': 2, 'postcrash': 6, 'goes': 8, '11.67': 2, 'new': 56, 'firms': 2, '4year': 2, 'jumps': 26, '1,600': 2, 'thin': 2, '24': 6, '30month': 4, 'earlier': 2, 'barrier': 12, 'slight': 2, 'climbs': 4, '2,100pt': 2, '912.28': 2, 'weak': 2, '26': 12, '27.3': 2, '47': 2, '16.43': 2, '11.86': 2, 'hectic': 2, '10.5': 4, 'buy': 2, 'rises': 18, 'abroad': 2, 'released': 2, '22.07': 2, '900': 10, 'plunges': 36, '19.61': 2, '33': 6, 'many': 2, 'months': 12, '750mark': 2, 'days': 2, '35': 6, '34': 14, '11.78': 2, 'massive': 2, '1,000point': 2, 'almost': 2, '23.9': 2, 'first': 6, 'managers': 2, 'wall': 8, '27.46': 2, '2,000': 10, 'zooms': 2, 'increased': 2, '11.11': 4, 'down': 92, '62': 2, '64': 2, '65': 2, 'another': 32, 'away': 2, '18month': 2, 'shoots': 4, 'lags': 2, '16.97': 4, 'punters': 2, 'market': 2, 'second': 2, '1,400': 8, 'passes': 12, 'fed': 2, '1,850': 2, 'takes': 6, 'puts': 2, '970': 2, 'support': 2, 'two': 2, 'mark': 36, 'retreats': 2, 'pc': 4, '24pt': 2, '8month': 2, 'low': 14, 'way': 2, '34.6': 2, '13.36': 2, '1,200': 2, 'markets': 4, 'bluechip': 2, '500': 2, 'lowest': 4, '12.28': 2, 'selling': 8, '1,922.89': 2, '650': 2, 'owest': 2, '39': 2, 'dips': 12, 'surges': 18, 'hits': 46, '2,400': 2, 'volume': 6, 'biggest': 6, 'wake': 2, '900mark': 4, 'demand': 4, 'prices': 2, 'despite': 2, 'highest': 6, 'reel': 2, '3month': 2, 'buying': 6, 'oneday': 4, '11': 40, 'jump': 2, '13': 18, 'crosses': 4, '15': 18, '14': 8, '17': 22, '16': 28, '19': 10, '18': 10, 'straight': 2, '31.62': 2, 'up': 178, 'hike': 2, 'record': 16, 'near': 2, 'nine': 2, '24.76': 2, 'turnover': 2, '31.68': 2, 'closing': 2, 'drops': 72, '1,878': 2, '400mark': 2, 'slips': 10, '30': 2, 'sees': 2, '600': 2, 'investors': 4, 'soars': 28, 'reacts': 2, 'december': 2, '26.9': 2, '1,500': 2, 'quiet': 4, 'high': 86, 'plunge': 8, 'firm': 2, 'rally': 2, '10.02': 2, 'hit': 8, 'blasts': 2, '400barrier': 2, '17.43': 2, 'reaches': 2, 'moves': 2, 'recovery': 2, '49': 2, '950': 4, '11.6': 2, '11.7': 2, '42': 2, '43': 2, '40': 2, '11.3': 2, '800points': 2, '1,300': 4, 'tumbles': 2, '10.99': 2, 'rupiah': 2, 'panic': 2, '11.21': 4, '1,370': 2, 'higher': 2, 'week': 2, '34.98': 2, 'extended': 2, 'crashes': 2, 'may': 2, 'dive': 2, 'time': 2, 'tops': 2, 'yesterday': 2, '38.24': 2, 'falls': 48, '15.57': 2, '25.4': 2, 'leaps': 6, 'peak': 10, '14.25': 2, '14.27': 2, '29': 8, '1,950': 2, '9.60': 2, 'rebound': 4, 'slides': 4, 'tokyo': 6, 'suffers': 4, '35.94': 2, 'third': 4, 'records': 2, 'light': 2, '16.66': 2, '31point': 2, 'cpf': 2, 'st': 2, '1,000': 8, '764.63': 2, 'points': 520, '12.13': 2, 'components': 74, 'sets': 2, '14,7': 2, 'fresh': 2, '800': 4, '44': 2, '1,240': 2, 'gain': 6})

In [635]:
for key in ccm.keys
    print key


---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-635-c0a4010bfc63> in <module>()
----> 1 for key in ccm.iterkeys()[:5]:
      2     print key

TypeError: 'dictionary-keyiterator' object has no attribute '__getitem__'

In [626]:
ccm_dict = []
for node in ccm:
    datadict = {}
    if type(node) == str:
        datadict['name'] = node
        word_list = []
        #datadict[node] = []
        counter = 0;
        for item in ccm[node].items():
            #d = {}
            #d['name'] = item[0]
            #d['size'] = item[1]
            #datadict[node].append(d)
            counter += item[1]
            word_list.append(item[0])
        datadict['links'] = word_list
        datadict['size'] = counter
    ccm_dict.append(datadict)

In [716]:
dataList = []
wordList = []
collected = []
for word in freqData['words']:
    wordList.append(word)
    
for node in ccm:
    collected.append(node)
    if node in wordList:
        for item in ccm[node].items():
            if item[0] not in collected:
                if item[0] in wordList:
                    word1 = node
                    word2 = item[0]
                    size = item[1]
                    dataList.append((word1,word2,size))
                else:
                    continue
            else:
                continue
    else:
        continue
        
ccm_csv = pd.DataFrame(dataList, columns=['word1','word2','size'])

In [722]:
ccm_csv.to_csv('cooccurrenceMatrixData.csv', encoding='utf-8')

In [729]:


In [779]:
wordList = []
collected = []
for word in freqData['words']:
    wordList.append(word)

collected = []
itemcollected = []
graphDict = dict()
graphDict['nodes'] = []
graphDict['links'] = []
nameDict = dict()
counter = -1
for node in ccm:
    if node in wordList:
        collected.append(node)
        if node not in itemcollected:
            counter += 1
            nodenum = counter
            nameDict[node] = nodenum
            nodeDict = dict()
            nodeDict['name'] = node
            nodeDict['group'] = 0
            nodeDict['id'] = nodenum
            graphDict['nodes'].append(nodeDict)
        else:
            nodenum = nameDict[node]
        itemcollected.append(node)
        for item in ccm[node].items():
            if item[0] not in collected:
                if item[0] in wordList:
                    if item[0] not in itemcollected:
                        counter += 1
                        itemnum = counter
                        nodeDict = dict()
                        nodeDict['name'] = item[0]
                        nodeDict['group'] = 0
                        nodeDict['id'] = itemnum
                        graphDict['nodes'].append(nodeDict)
                        itemcollected.append(item[0])
                        nameDict[item[0]] = itemnum             
                    linkDict = dict()
                    linkDict['source'] = nameDict[item[0]]
                    linkDict['target'] = nodenum
                    linkDict['value'] = item[1]
                    linkDict['name1'] = node
                    linkDict['name2'] = item[0]
                    graphDict['links'].append(linkDict)

In [780]:
import json
with open('newsGraph.json', 'w') as f:
    f.write(json.dumps(graphDict))

In [777]:
len(graphDict['nodes'])


Out[777]:
100

In [776]:
graphDict


Out[776]:
{'links': [{'source': 1, 'target': 0, 'value': 40},
  {'source': 2, 'target': 0, 'value': 44},
  {'source': 3, 'target': 0, 'value': 88},
  {'source': 4, 'target': 0, 'value': 14},
  {'source': 5, 'target': 0, 'value': 26},
  {'source': 6, 'target': 0, 'value': 14},
  {'source': 7, 'target': 0, 'value': 18},
  {'source': 8, 'target': 0, 'value': 40},
  {'source': 9, 'target': 0, 'value': 18},
  {'source': 10, 'target': 0, 'value': 186},
  {'source': 11, 'target': 0, 'value': 12},
  {'source': 12, 'target': 0, 'value': 58},
  {'source': 13, 'target': 0, 'value': 22},
  {'source': 14, 'target': 0, 'value': 10},
  {'source': 15, 'target': 0, 'value': 32},
  {'source': 16, 'target': 0, 'value': 22},
  {'source': 17, 'target': 0, 'value': 40},
  {'source': 18, 'target': 0, 'value': 18},
  {'source': 19, 'target': 0, 'value': 4},
  {'source': 20, 'target': 0, 'value': 84},
  {'source': 21, 'target': 0, 'value': 36},
  {'source': 22, 'target': 0, 'value': 32},
  {'source': 23, 'target': 0, 'value': 64},
  {'source': 24, 'target': 0, 'value': 30},
  {'source': 25, 'target': 0, 'value': 38},
  {'source': 26, 'target': 0, 'value': 34},
  {'source': 27, 'target': 0, 'value': 10},
  {'source': 28, 'target': 0, 'value': 8},
  {'source': 29, 'target': 0, 'value': 34},
  {'source': 30, 'target': 0, 'value': 68},
  {'source': 31, 'target': 0, 'value': 28},
  {'source': 32, 'target': 0, 'value': 6},
  {'source': 33, 'target': 0, 'value': 82},
  {'source': 34, 'target': 0, 'value': 38},
  {'source': 35, 'target': 0, 'value': 32},
  {'source': 36, 'target': 0, 'value': 82},
  {'source': 37, 'target': 0, 'value': 56},
  {'source': 38, 'target': 0, 'value': 12},
  {'source': 39, 'target': 0, 'value': 94},
  {'source': 40, 'target': 0, 'value': 22},
  {'source': 41, 'target': 0, 'value': 18},
  {'source': 42, 'target': 0, 'value': 32},
  {'source': 43, 'target': 0, 'value': 24},
  {'source': 44, 'target': 0, 'value': 30},
  {'source': 45, 'target': 0, 'value': 10},
  {'source': 46, 'target': 0, 'value': 28},
  {'source': 47, 'target': 0, 'value': 38},
  {'source': 48, 'target': 0, 'value': 14},
  {'source': 49, 'target': 0, 'value': 40},
  {'source': 50, 'target': 0, 'value': 22},
  {'source': 51, 'target': 0, 'value': 8},
  {'source': 52, 'target': 0, 'value': 126},
  {'source': 53, 'target': 0, 'value': 44},
  {'source': 54, 'target': 0, 'value': 22},
  {'source': 55, 'target': 0, 'value': 18},
  {'source': 56, 'target': 0, 'value': 12},
  {'source': 57, 'target': 0, 'value': 82},
  {'source': 58, 'target': 0, 'value': 34},
  {'source': 59, 'target': 0, 'value': 18},
  {'source': 60, 'target': 0, 'value': 46},
  {'source': 61, 'target': 0, 'value': 36},
  {'source': 62, 'target': 0, 'value': 32},
  {'source': 63, 'target': 0, 'value': 34},
  {'source': 64, 'target': 0, 'value': 20},
  {'source': 65, 'target': 0, 'value': 32},
  {'source': 66, 'target': 0, 'value': 38},
  {'source': 67, 'target': 0, 'value': 10},
  {'source': 68, 'target': 0, 'value': 32},
  {'source': 69, 'target': 0, 'value': 52},
  {'source': 70, 'target': 0, 'value': 34},
  {'source': 71, 'target': 0, 'value': 116},
  {'source': 72, 'target': 0, 'value': 76},
  {'source': 73, 'target': 0, 'value': 30},
  {'source': 74, 'target': 0, 'value': 16},
  {'source': 75, 'target': 0, 'value': 46},
  {'source': 76, 'target': 0, 'value': 36},
  {'source': 77, 'target': 0, 'value': 34},
  {'source': 78, 'target': 0, 'value': 60},
  {'source': 79, 'target': 0, 'value': 10},
  {'source': 80, 'target': 0, 'value': 28},
  {'source': 81, 'target': 0, 'value': 64},
  {'source': 82, 'target': 0, 'value': 8},
  {'source': 83, 'target': 0, 'value': 40},
  {'source': 84, 'target': 0, 'value': 14},
  {'source': 85, 'target': 0, 'value': 44},
  {'source': 86, 'target': 0, 'value': 50},
  {'source': 87, 'target': 0, 'value': 10},
  {'source': 88, 'target': 0, 'value': 40},
  {'source': 89, 'target': 0, 'value': 12},
  {'source': 90, 'target': 0, 'value': 6},
  {'source': 91, 'target': 0, 'value': 10},
  {'source': 92, 'target': 0, 'value': 4},
  {'source': 93, 'target': 0, 'value': 22},
  {'source': 1, 'target': 38, 'value': 16},
  {'source': 12, 'target': 38, 'value': 16},
  {'source': 2, 'target': 38, 'value': 12},
  {'source': 3, 'target': 38, 'value': 26},
  {'source': 4, 'target': 38, 'value': 2},
  {'source': 5, 'target': 38, 'value': 12},
  {'source': 7, 'target': 38, 'value': 8},
  {'source': 8, 'target': 38, 'value': 14},
  {'source': 9, 'target': 38, 'value': 6},
  {'source': 10, 'target': 38, 'value': 54},
  {'source': 86, 'target': 38, 'value': 12},
  {'source': 13, 'target': 38, 'value': 8},
  {'source': 14, 'target': 38, 'value': 10},
  {'source': 15, 'target': 38, 'value': 10},
  {'source': 16, 'target': 38, 'value': 4},
  {'source': 17, 'target': 38, 'value': 14},
  {'source': 18, 'target': 38, 'value': 8},
  {'source': 21, 'target': 38, 'value': 8},
  {'source': 22, 'target': 38, 'value': 8},
  {'source': 23, 'target': 38, 'value': 12},
  {'source': 19, 'target': 38, 'value': 2},
  {'source': 25, 'target': 38, 'value': 4},
  {'source': 26, 'target': 38, 'value': 12},
  {'source': 27, 'target': 38, 'value': 12},
  {'source': 28, 'target': 38, 'value': 2},
  {'source': 29, 'target': 38, 'value': 8},
  {'source': 30, 'target': 38, 'value': 24},
  {'source': 31, 'target': 38, 'value': 8},
  {'source': 33, 'target': 38, 'value': 10},
  {'source': 34, 'target': 38, 'value': 8},
  {'source': 35, 'target': 38, 'value': 10},
  {'source': 36, 'target': 38, 'value': 36},
  {'source': 37, 'target': 38, 'value': 26},
  {'source': 39, 'target': 38, 'value': 26},
  {'source': 40, 'target': 38, 'value': 8},
  {'source': 41, 'target': 38, 'value': 6},
  {'source': 42, 'target': 38, 'value': 12},
  {'source': 43, 'target': 38, 'value': 6},
  {'source': 44, 'target': 38, 'value': 8},
  {'source': 45, 'target': 38, 'value': 2},
  {'source': 46, 'target': 38, 'value': 4},
  {'source': 47, 'target': 38, 'value': 10},
  {'source': 48, 'target': 38, 'value': 4},
  {'source': 49, 'target': 38, 'value': 16},
  {'source': 50, 'target': 38, 'value': 2},
  {'source': 51, 'target': 38, 'value': 2},
  {'source': 53, 'target': 38, 'value': 16},
  {'source': 54, 'target': 38, 'value': 8},
  {'source': 55, 'target': 38, 'value': 6},
  {'source': 56, 'target': 38, 'value': 4},
  {'source': 57, 'target': 38, 'value': 12},
  {'source': 58, 'target': 38, 'value': 10},
  {'source': 20, 'target': 38, 'value': 30},
  {'source': 59, 'target': 38, 'value': 2},
  {'source': 60, 'target': 38, 'value': 6},
  {'source': 61, 'target': 38, 'value': 6},
  {'source': 63, 'target': 38, 'value': 10},
  {'source': 52, 'target': 38, 'value': 34},
  {'source': 64, 'target': 38, 'value': 8},
  {'source': 65, 'target': 38, 'value': 4},
  {'source': 66, 'target': 38, 'value': 10},
  {'source': 67, 'target': 38, 'value': 6},
  {'source': 68, 'target': 38, 'value': 8},
  {'source': 69, 'target': 38, 'value': 12},
  {'source': 70, 'target': 38, 'value': 8},
  {'source': 71, 'target': 38, 'value': 36},
  {'source': 72, 'target': 38, 'value': 4},
  {'source': 73, 'target': 38, 'value': 16},
  {'source': 74, 'target': 38, 'value': 2},
  {'source': 75, 'target': 38, 'value': 20},
  {'source': 76, 'target': 38, 'value': 8},
  {'source': 77, 'target': 38, 'value': 6},
  {'source': 78, 'target': 38, 'value': 14},
  {'source': 79, 'target': 38, 'value': 2},
  {'source': 80, 'target': 38, 'value': 2},
  {'source': 24, 'target': 38, 'value': 10},
  {'source': 81, 'target': 38, 'value': 2},
  {'source': 82, 'target': 38, 'value': 4},
  {'source': 83, 'target': 38, 'value': 10},
  {'source': 84, 'target': 38, 'value': 6},
  {'source': 85, 'target': 38, 'value': 12},
  {'source': 87, 'target': 38, 'value': 4},
  {'source': 88, 'target': 38, 'value': 10},
  {'source': 89, 'target': 38, 'value': 2},
  {'source': 62, 'target': 38, 'value': 10},
  {'source': 90, 'target': 38, 'value': 2},
  {'source': 92, 'target': 38, 'value': 8},
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  {'group': 0, 'name': 'fire'},
  {'group': 0, 'name': 'big'},
  {'group': 0, 'name': 'back'},
  {'group': 0, 'name': 'up'},
  {'group': 0, 'name': 'us'},
  {'group': 0, 'name': 'home'},
  {'group': 0, 'name': 'peace'},
  {'group': 0, 'name': 'go'},
  {'group': 0, 'name': 'lee'},
  {'group': 0, 'name': 'reds'},
  {'group': 0, 'name': 'tengku'},
  {'group': 0, 'name': 'next'},
  {'group': 0, 'name': 'hit'},
  {'group': 0, 'name': 'ties'},
  {'group': 0, 'name': 'chief'},
  {'group': 0, 'name': 'crash'},
  {'group': 0, 'name': 'day'},
  {'group': 0, 'name': 'red'},
  {'group': 0, 'name': 'ban'},
  {'group': 0, 'name': 'times'},
  {'group': 0, 'name': 'attack'},
  {'group': 0, 'name': 'move'},
  {'group': 0, 'name': 'latest'},
  {'group': 0, 'name': 'government'},
  {'group': 0, 'name': 'page'},
  {'group': 0, 'name': '\xe2\x80\x94'},
  {'group': 0, 'name': 'cut'},
  {'group': 0, 'name': 'index'},
  {'group': 0, 'name': 'column'},
  {'group': 0, 'name': 'dow'},
  {'group': 0, 'name': 'jones'},
  {'group': 0, 'name': 'miscellaneous'}]}

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