This notebook builds on our earlier investigations:
Previous literature and our pilot study both support the idea that health risks are both particularly and increasingly prominent. We want to test that in our 60 million word U.S. print news corpus.
As in our previous investigation, we begin by importing corpkit, setting paths to corpora, and the like.
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# show plots in this notebook
%matplotlib inline
import os
# import corpkit
from corpkit import interrogator, editor, plotter, conc
# some wordlists we'll use later
from dictionaries.process_types import processes
from dictionaries.wordlists import wordlists
from dictionaries.roles import roles
# for editing/combining results:
import pandas as pd
pd.options.display.max_rows = 30
pd.options.display.max_columns = 6
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nyt = 'data/NYT-parsed'
wsj = 'data/WSJ-parsed'
wap = 'data/WAP-parsed'
cht = 'data/CHT-parsed'
ust = 'data/UST-parsed'
tbt = 'data/TBT-parsed'
all_corpora = [nyt, wsj, wap, cht, ust, tbt]
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from corpkit import load_result
allwords = load_result('6_allwords_newest')
riskwords = load_result('6_riskwords_newest')
riskclasses = load_result('6_riskclasses_newest')
risktags = load_result('6_risktags_newest')
n_risk = load_result('6_n_risk')
risk_of = load_result('6_risk_of')
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riskword = r'(?i)\brisk'
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# noun in NP in PP headed by of in NP headed by nominal risk
risk_of_query = r'/NN.?/ >># (NP > (PP <<# /(?i)of/ > (NP <<# (/NN.?/ < /(?i).?\brisk.?/))))'
risk_of = interrogator(corpora, 'words', risk_of_query, lemmatise = True,
num_proc = 3, quicksave = '6_risk_of')
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risk_of['CHT'].results[:10]
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n_risk_query = r'/NN.?/ !># NP > (NP <<# (/NN.?/ < /(?i).?\brisk.?/))'
n_risk = interrogator(corpora, 'words', n_risk_query, lemmatise = True,
num_proc = 3, quicksave = '6_n_risk')
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n_risk['WAP'].results[:10]
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As in our last investigation, we can define a few helper functions to collapse distinctions betwee newspapers, years and entries:
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def collapsed(interrogation):
"""collapse distinction between newspapers"""
import pandas as pd
interroformat = False
if type(interrogation.values()[0]) == pd.core.frame.DataFrame:
dat = interrogation.values()[0]
else:
dat = interrogation.values()[0].results
interroformat = True
order = list(dat.columns)
for i in interrogation.values()[1:]:
if interroformat:
dat = dat.add(i.results, fill_value = 0)
else:
dat = dat.add(i, fill_value = 0)
return dat[order]
def entry_across_corpora(result_dict, entry_name, regex = False):
"""get a single entry as a dataframe"""
import pandas as pd
import re
res = []
# for each corpus name and data
for k, v in sorted(result_dict.items()):
# grab the process result for each paper
if not regex:
try:
column = v.results[entry_name]
except:
continue
else:
column = v.results[[c for c in list(v.results.columns) if re.search(entry_name, c)]].iloc[:,0]
# rename it to the corpus name
column.name = k
# append to a list
res.append(column)
# concatenate and return
return pd.concat(res, axis = 1)
def get_totals(interrogation):
"""helper function: get totals from dict of interrogations"""
lst = []
# for each interrogation name and data
for k, v in interrogation.items():
# get the totals
tot = v.totals
# name the totals with the newspaper
tot.name = k.upper()
# add to a list
lst.append(tot)
# turn the list into a dataframe
return pd.concat(lst, axis = 1)
With these interrogations and functions, we have everything we need to do some visualisation.
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rel_coll = editor(collapsed(risk_of), '%', 'self', print_info = False)
inc_rel_coll = editor(collapsed(risk_of), '%', 'self', sort_by = 'increase', print_info = False)
dec_rel_coll = editor(collapsed(risk_of), '%', 'self', sort_by = 'decrease', print_info = False)
plotter('Risk of \emph{noun}', rel_coll.results)
plotter('Risk of \emph{noun}, increasing', inc_rel_coll.results)
plotter('Risk of \emph{noun}, decreasing', dec_rel_coll.results)
What jumps out here, of course, is the risk of attack, which rises quickly in popularity at the turn of the millenium. Let's look at this result across newspapers:
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attack = entry_across_corpora(risk_of, 'attack')
attack[:10]
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rel_attack = editor(attack, '%', get_totals(risk_of), print_info = False)
plotter('Risk of attack', rel_attack.results, subplots = True, layout = (2,3), figsize = (9,6), save = 'rattack')
... but, it's not what you think. While it's perhaps only natural that we assume that this construction occurs in the context of terrorism, closer inspection shows us something different:
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r_of_attack = r'NP <<# (/NN.?/ < /(?i)\battack/) > (PP <<# /(?i)of/ > (NP <<# (/NN.?/ < /(?i).?\brisk.?/)))'
lines = conc(os.path.join(wap, '2004'), 't', r_of_attack, print_output = False, random = True)
lines[['l', 'm', 'r']]
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Risk patterns with health topics much more than we seem to assume it does. In fact, if we do two searches, for risk of heart attack and risk of terror attack, we can measure the difference:
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rel_coll = editor(collapsed(n_risk), '%', 'self', print_info = False)
plotter('\emph{Noun} risk', rel_coll.results)
The important thing to note is that these two categories have overlap, but are not grammatically interchangable. You can say "the risk of cancer" or "the cancer risk", but you can't say "the risk of health".
Semantically, only the negative outcome can occur in both positions: the risked thing can only occur before the risk.
Fortunately for us, it's unambiguous whether or not a word is the negative outcome or the risked thing. Furthermore, we can apply grammatical tests to be certain:
1a. I risked my life
1b. I risked losing my life
2a. I risked death
2b. * I risked losing death
So, let's make a simple list:
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riskedthing = ['life', 'everything', 'money', 'career', 'health', 'lot',
'reputation', 'capital', 'future', 'job', 'safety', 'credit', 'currency',
'possibility', 'anything', 'return', 'neck', 'nothing']
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Let's combine the two results (knowing full well that they aren't perfect equivalents):
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riskthings = {}
for name, data in risk_of.items():
df = data.results.add(n_risk[name].results, fill_value = 0)
df.ix['total'] = df.sum()
df = df.T.sort('total', ascending = False).T
df = df.drop('total', axis = 0)
riskthings[name] = df
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#collapsed(riskthings)
rel_coll = editor(collapsed(riskthings), '%', 'self')
plotter('Risk of \emph{noun}\slash \emph{noun} risk', rel_coll.results)
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We can define some fairly unambiguous wordlists, too:
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cats = {'Health': ['cancer', 'health', 'attack', 'disease', 'death', 'infection', 'heart', 'stroke', 'injury',
'smoking', 'clot', 'complication', 'heart-attack', 'diabetes', 'fracture', 'drug', 'bleeding',
'suicide', 'contagion', 'mortality', 'illness', 'aid', 'breast', 'miscarriage', 'dementia',
'radiation', 'surgery', 'life', 'depression', 'osteoporosis', 'pregnancy', 'breast-cancer',
'birth', 'disorder', 'leukemia'],
'Finance': ['credit', 'inflation', 'default', 'market', 'currency', 'investment', 'interest-rate', 'recession',
'business', 'deflation', 'price', 'return', 'litigation', 'rate', 'cost', 'loan', 'counterparty',
'trading', 'portfolio', 'stock', 'liability', 'prepayment', 'fund', 'bond', 'growth', 'asset',
'decline', 'liquidity', 'lending', 'volatility', 'bank', 'fraud', 'insurance', 'mortgage', 'rollover',
'company', 'accident', 'debt', 'interest', 'bankruptcy', 'slowdown', 'expense',
'collapse', 'foreign-exchange', 'deal', 'downturn', 'transmission',
'foreclosure', 'exchange-rate', 'exchange', 'bubble', 'derivative',
'stock-market', 'devaluation', 'capital', 'pressure', 'downgrade',
'fluctuation', 'investor', 'crash', 'taxpayer', 'economy', 'investing', 'dollar',
'tax', 'national-security', 'equity', 'trade', 'transaction', 'money', 'career',
'executive', 'industry', 'run', 'insolvency', 'underwriting', 'claim', 'settlement']}
uncategorised = [u'downside', 'security', 'loss', 'benefit', 'safety', 'failure', 'problem', 'reward', 'event',
'war', 'flight', 'damage', 'effect', 'course', 'exposure', 'fire', 'country', 'policy', 'control',
'execution', 'u.s', 'increase', 'crisis', 'terrorism', 'defect', 'harm', 'uncertainty',
'lifetime', 'strategy', 'product', 'change', 'recurrence', 'conflict', 'lawsuit', 'contamination',
'government', 'development', 'disruption', 'move', 'opportunity', 'action', 'violence', 'error',
'mr', 'theft', 'use', 'type', 'inventory', 'treatment', 'explosion', 'proliferation', 'headline',
'behavior', 'project', 'material', 'risk', 'china', 'delay', 'weakness', 'abuse', 'earthquake',
'backlash', 'flooding', 'correction', 'approach', 'integration', 'strike', 'reaction', 'collision',
'spill', 'program', 'casualty', 'shock', 'system', 'hurricane', 'flood', 'challenge', 'instability',
'longevity', 'activity', 'catastrophe', 'overheating', 'issue', 'disaster', 'cut', 'property',
'meltdown']
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#get_totals(risk_of)
comp_n_risk = editor(n_risk, merge_entries = cats, just_entries = cats.keys())
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rel_tot = editor(collapsed(comp_risk_of), '%', get_totals(risk_of),
use_df2_totals = True, sort_by = 'total')
plotter('Health and financial risks', rel_tot.results, style = 'seaborn-notebook')
Breaking this down by publication, we can expect to see more financially oriented newspapers focussing more on financial risks:
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rel_comp_risk_of = editor(risk_of, '%', risk_of, use_df2_totals = True, \
merge_entries = cats, just_entries = cats.keys(), print_info = False)
for name, data in rel_comp_risk_of.items():
plotter('Health and financial risk in the %s' % name, data.results,
style = 'seaborn-notebook', figsize = (6, 3), legend_pos = 'upper right')
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