In [29]:
%matplotlib inline
import sys
#sys.path.append('/home/fin/Documents/datamining_hansard/dh/lib/python3.4/site-packages/')
import nltk
import nltk.tokenize
import itertools
import glob
import lxml
import lxml.html
import requests
import os
import csv
import wordcloud
import matplotlib.pyplot as plt
from scipy.misc import imread
import numpy as np
import wordcloud
In [30]:
page = requests.get("http://www.theyworkforyou.com/pwdata/scrapedxml/debates/")
tree = lxml.html.fromstring(page.text)
debates = tree.xpath('//a/text()')
# Find the filenames of all the daily hansard xmls for this parliament
debates2015 = [x for x in debates if x.startswith('debates2015')]
new_parliament = [x for x in debates2015 if int(x.split('-')[1]) > 4]
def download_file(url, folder):
"""
Download a given url into specified folder
"""
local_filename = url.split('/')[-1]
if not os.path.exists(os.path.join(folder, local_filename)):
r = requests.get(url, stream=True)
with open(os.path.join(folder, local_filename), 'wb') as f:
for chunk in r.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
f.flush()
return local_filename
In [31]:
# download xmls
for xml in new_parliament:
download_file("http://www.theyworkforyou.com/pwdata/scrapedxml/debates/"+xml,
"data")
Generate 3 datastructs from xml and mp_data.csv mentioned below:
named_text (a list of tuples of MPs names and their speeches over time i.e. will appear multiple times if they speak several times) words_spoken_by_mp (a dict keyed with the MPs name and valued by a tuple representing their party and concatenated text)party_talk (a dict keyed by the party name and valued by concatenated text of all speeches for all MPs belonging to it)Additionally create the ancillary dict parties which contains the list of "real parties" and their respective number of MPs and the party_dict containing all MPs respective parties
In [32]:
def get_speaker_and_text(xml):
"""
For a given xml file return a list of tuples
of speeches and their speakers
"""
with open(xml, 'rb') as fh:
parsed_xml = fh.read()
data = []
root = lxml.etree.fromstring(parsed_xml)
speeches = root.xpath('speech')
for speech in speeches:
#if speech.get('nospeaker') is not None:
speech_list = [x.text for x in speech.getchildren() if x.text is not None]
speech_list = " ".join(speech_list)
speaker = speech.get('speakername')
data.append((speaker, speech_list))
return data
In [37]:
named_text = []
for xml in glob.glob(os.path.join("data", "*.xml")):
named_text = named_text + get_speaker_and_text(xml)
In [40]:
named_text[-1]
Out[40]:
('Jess Phillips',
'Thank you, Mr Deputy Speaker, for giving me the opportunity to make my maiden speech. This is the first time I have been called a maiden, and it seems a little unusual as my children watch me from the Strangers Gallery. I assure everybody here that it is good that they are behind the glass—I can see them talking up there! I am exceptionally proud to be one of the new women MPs elected to this Parliament, and to be one of the working parents elected—we have a lot to offer when deciding what is best for our country, which has not always happened. Like those of my predecessor, John Hemming, my roots in Birmingham, Yardley run deep. I know from reading his maiden speech that this was a source of great pride for him—and I feel the same. A Brummie accent is a rare thing in this Chamber, and I look forward to changing that. Like so many Brummies, my nan and grandad from both my mother’s and father’s sides moved from Birmingham’s Peaky Blinders-famed inner city industrial areas in Small Heath, Ladywood and Winson Green to the estates of Yardley and Sheldon that were newly built in the ’30s and ’40s. They were proud of their new homes and raised all their children there. My family benefited from decent council houses and good communities, and during my campaign I was proud to knock on the doors of three different houses where my parents had lived—on Garretts Green Lane, Frodesley Road and Gleneagles Road. During the campaign, I visited Yardley Great Trust, a charity whose history in the area is even longer than mine. Originating in the 14th century, the Yardley Great Trust has helped to alleviate poverty and support residents in their sickness and old age, and it continues to do so today. In 1966, the Yardley Great Trust gave my mum a grant to help poor local kids stay in education, so that she did not have to leave school to help her single mother with the housekeeping before doing her A-levels. She went on to achieve a great many things and gave me and my brothers a good life and lots of opportunities. Birmingham, Yardley was good to my family, and I plan to repay this debt. I requested to make my maiden speech in this section of the Queen’s Speech debates because, along with all things Birmingham, Yardley, I am deeply committed to improving our country’s response to victims of domestic and sexual violence and abuse in all its forms. Having worked for years in a service that operated refuges, rape crisis, child sexual exploitation services and human trafficking services, I know that we need to do more. We need look no further than at the poor rape conviction rates to know that for very vulnerable victims our justice system is too often just another establishment that has failed to protect them. For years before I sat on these Green Benches—and, I am sure, for many more after—there will have been calls for Government Departments to work better together to understand the multiple and layered effects that our decisions have on people’s lives. I can think of no better example than the interaction between the Department for Work and Pensions and our Justice and Home Affairs Departments. I have no doubt that the Home Secretary is committed to ending child sexual exploitation, and it is true to say that her Department has invested in improving services for victims of sexual violence. However, as a Government, it is no good to give with one hand and take away with another. This Government’s rumoured plans to remove housing benefit from people aged under 21 will be disastrous for these vulnerable victims. While I do not agree with this measure at all, I want to compel the Government to remove from this new legislation vulnerable people in supported accommodation. To make my point, I shall tell the story of Helen. Helen was an 18-year-old girl I met in my first week in my old job, and she has stayed with me for the last six years. She had been abused by her father as a child, and had been in and out of local authority care throughout her—so-called—childhood. Between the ages of 15 and 18, she had been exploited by one “boyfriend” or another, all of them much older than her and none with her best interests at heart. Helen had come to ask for support from our services following her abuse. She was being supported by an independent sexual violence adviser so that she could be helped to give statements to the police, and seek convictions and justice for her childhood abuse. However, following the breakdown of her relationship with her mother and, subsequently, her grandmother, she became homeless, and her life was difficult and chaotic. In the absence of a stable living environment, she again fell victim to those who abused her, and fell out of the justice system. Eventually—after cycles of absconding and returning —we were able to secure supported living for Helen. I remember driving her to what is now my constituency to take her to St Basil’s, a brilliant youth homelessness charity. With the aid of housing benefit and the home she was given, she was able to learn to look after herself and, with support, seek justice in order to move on with her life and make a future. Where will Helen go now, in a future that will not give her housing benefit? How many A and E visits will she make during this Parliament? How many custody suites will she block up? How many police reports will be filed for her by a stretched force? Worse still, how many other people will be abused by those who abused Helen while justice is not done? If that is not enough to alarm the justice and home affairs teams who are sitting opposite me, perhaps I should put it differently: how much will this cost their Departments? In the last Parliament, the domestic violence lobby was able to ensure that victims living in refuges were exempt from universal credit and the benefits cap. Although the Government’s decision was an afterthought, it was the right decision to keep women and children safe. I urge this Government not to make the same mistake of making the most vulnerable young people an afterthought. I urge them to exempt young people who are at risk of homelessness, and those who are in supported accommodation, from their welfare reform. Last year, 25% of the victims who lived in Birmingham and Solihull Women’s Aid refuges alone were aged between 18 and 21. We must protect these victims. Justice and security do not end in their defined Government Departments. In my constituency, there are lots of Helens, and it is my job to amplify their stories so that we stop finding it easy to look away. I will never look away. I thank you, Mr Deputy Speaker, for the chance to tell that story, and I look forward to telling many more.')
Using mp_data.csv manually downloaded from http://www.theyworkforyou.com/mps/?f=csv to acquire party affiliations for each mp and generate a dict for quick lookup of an MP's party using their name
In [6]:
#Encoding issue thus the weird kwarg
with open('data/mp_data.csv', encoding = "ISO-8859-1") as fh:
mp_data = [x for x in csv.reader(fh)]
party_dict = {}
for mp in mp_data[1:]:
# forename and surname fields
name = mp[1] + ' ' + mp[2]
party = mp[3]
party_dict.update({name: party})
#Manual clean up of this data
#Add the speakers
party_dict.update({'Mr Speaker': 'Speaker'})
party_dict.update({'Mr Deputy Speaker': 'Speaker'})
party_dict.update({'Mr Speaker-Elect': 'Speaker'})
party_dict.update({'The Chairman': 'Speaker'})
party_dict.update({'The Second Deputy Chairman': 'Speaker'})
party_dict.update({'Madam Deputy Speaker': 'Speaker'})
#add Stephen Philip Rotheram for some reason
party_dict.update({'Stephen Philip Rotheram': 'Labour'})
#add multiple speakers
party_dict.update({'My Lords and Members of the House of Commons':'Multiple'})
party_dict.update({'Members of the House of Commons':'Multiple'})
party_dict.update({'Hon. Members':'Multiple'})
party_dict.update({'Several hon. Members':'Multiple'})
#add none
party_dict.update({None: 'None'})
# parties and MP number
parties = {'UKIP': 1,
'Sinn Féin': 4,
'Conservative': 330,
'Labour': 232,
'Liberal Democrat': 8,
'Independent': 1,
'UUP': 2,
'DUP': 8,
'Scottish National Party': 56,
'Green': 1,
'Social Democratic and Labour Party': 3,
'Plaid Cymru': 3}
In [74]:
words_spoken_by_mp = {}
for speaker, text in named_text:
text = text.replace("“", "\"")
party = party_dict[speaker]
# remove cruft "parties"
if party == 'None' or party == 'Multiple' or party == 'Speaker':
continue
# collapse labour co-op and labour down to one party
if party == 'Labour/Co-operative':
party = 'Labour'
if speaker in words_spoken_by_mp.keys():
words_spoken_by_mp[speaker] = (party, words_spoken_by_mp[speaker][1] + ' ' + text)
else:
words_spoken_by_mp.update({speaker: (party, text)})
In [87]:
from nltk.tokenize import word_tokenize, sent_tokenize, RegexpTokenizer
from nltk.stem.wordnet import WordNetLemmatizer
In [83]:
def text_string_to_vocab(speech_string):
"""
Get unique vocabularly for a given string of transcribed
text.
Specifically, tokenize this to remove all vocabularly (i.e. just find strings
of alphanumerical characters) resulting in a list of tokenized words
Then use the WordNet Lemmatizer for lemmatization
Convert this lemmatized list into a set to remove redundancy
and return the length of the non-redundant lemmatization
"""
lemma = WordNetLemmatizer()
tokenizer = RegexpTokenizer(r'\w+')
lemmatized_set = set([lemma.lemmatize(word) for word in tokenizer.tokenize(speech_string)])
return len(lemmatized_set)
# get MP vocab size in the same format as words_spoken_by_mp but replacing text with len of lemmatized speech
vocab_by_MP = {}
for MP in words_spoken_by_mp.keys():
vocab_by_MP.update({MP : (words_spoken_by_mp[MP][0], text_string_to_vocab(words_spoken_by_mp[MP][1]))})
In [105]:
d3_js_data = []
for MP in vocab_by_MP.keys():
d3_js_data.append('{{MP: "{0}", id:"{3}", x: .{1}, r: .45, colour: "{2}", vocabulary: "{1}", party: "{2}"}}'.format(MP, vocab_by_MP[MP][1], vocab_by_MP[MP][0], MP.replace('.','').replace(' ','_').replace("'", '')))
d3_data = ",".join(d3_js_data)
In [106]:
d3_data
Out[106]:
'{MP: "Edward Leigh", id:"Edward_Leigh", x: .3569, r: .45, colour: "Conservative", vocabulary: "3569", party: "Conservative"},{MP: "Michelle Donelan", id:"Michelle_Donelan", x: .1182, r: .45, colour: "Conservative", vocabulary: "1182", party: "Conservative"},{MP: "Jake Berry", id:"Jake_Berry", x: .1477, r: .45, colour: "Conservative", vocabulary: "1477", party: "Conservative"},{MP: "Holly Lynch", id:"Holly_Lynch", x: .928, r: .45, colour: "Labour", vocabulary: "928", party: "Labour"},{MP: "Gloria De Piero", id:"Gloria_De_Piero", x: .823, r: .45, colour: "Labour", vocabulary: "823", party: "Labour"},{MP: "Roberta Blackman-Woods", id:"Roberta_Blackman-Woods", x: .1026, r: .45, colour: "Labour", vocabulary: "1026", party: "Labour"},{MP: "Henry Smith", id:"Henry_Smith", x: .1162, r: .45, colour: "Conservative", vocabulary: "1162", party: "Conservative"},{MP: "Neil Gray", id:"Neil_Gray", x: .1810, r: .45, colour: "Scottish National Party", vocabulary: "1810", party: "Scottish National Party"},{MP: "Chuka Umunna", id:"Chuka_Umunna", x: .2030, r: .45, colour: "Labour", vocabulary: "2030", party: "Labour"},{MP: "Chris White", id:"Chris_White", x: .475, r: .45, colour: "Conservative", vocabulary: "475", party: "Conservative"},{MP: "Yvonne Fovargue", id:"Yvonne_Fovargue", x: .956, r: .45, colour: "Labour", vocabulary: "956", party: "Labour"},{MP: "Mims Davies", id:"Mims_Davies", x: .2117, r: .45, colour: "Conservative", vocabulary: "2117", party: "Conservative"},{MP: "Marion Fellows", id:"Marion_Fellows", x: .1169, r: .45, colour: "Scottish National Party", vocabulary: "1169", party: "Scottish National Party"},{MP: "Pauline Latham", id:"Pauline_Latham", x: .1257, r: .45, colour: "Conservative", vocabulary: "1257", party: "Conservative"},{MP: "Will Quince", id:"Will_Quince", x: .1456, r: .45, colour: "Conservative", vocabulary: "1456", party: "Conservative"},{MP: "Mark Hendrick", id:"Mark_Hendrick", x: .459, r: .45, colour: "Labour", vocabulary: "459", party: "Labour"},{MP: "Laurence Robertson", id:"Laurence_Robertson", x: .673, r: .45, colour: "Conservative", vocabulary: "673", party: "Conservative"},{MP: "Mary Robinson", id:"Mary_Robinson", x: .1001, r: .45, colour: "Conservative", vocabulary: "1001", party: "Conservative"},{MP: "Pat Glass", id:"Pat_Glass", x: .1000, r: .45, colour: "Labour", vocabulary: "1000", party: "Labour"},{MP: "Luciana Berger", id:"Luciana_Berger", x: .1216, r: .45, colour: "Labour", vocabulary: "1216", party: "Labour"},{MP: "Brandon Lewis", id:"Brandon_Lewis", x: .1808, r: .45, colour: "Conservative", vocabulary: "1808", party: "Conservative"},{MP: "Rob Marris", id:"Rob_Marris", x: .2523, r: .45, colour: "Labour", vocabulary: "2523", party: "Labour"},{MP: "Rebecca Long-Bailey", id:"Rebecca_Long-Bailey", x: .1858, r: .45, colour: "Labour", vocabulary: "1858", party: "Labour"},{MP: "Clive Lewis", id:"Clive_Lewis", x: .1516, r: .45, colour: "Labour", vocabulary: "1516", party: "Labour"},{MP: "Peter Kyle", id:"Peter_Kyle", x: .1456, r: .45, colour: "Labour", vocabulary: "1456", party: "Labour"},{MP: "Geoffrey Robinson", id:"Geoffrey_Robinson", x: .590, r: .45, colour: "Labour", vocabulary: "590", party: "Labour"},{MP: "Stephen Timms", id:"Stephen_Timms", x: .1881, r: .45, colour: "Labour", vocabulary: "1881", party: "Labour"},{MP: "Greg Knight", id:"Greg_Knight", x: .304, r: .45, colour: "Conservative", vocabulary: "304", party: "Conservative"},{MP: "Alistair Carmichael", id:"Alistair_Carmichael", x: .2581, r: .45, colour: "Liberal Democrat", vocabulary: "2581", party: "Liberal Democrat"},{MP: "Andrew Turner", id:"Andrew_Turner", x: .661, r: .45, colour: "Conservative", vocabulary: "661", party: "Conservative"},{MP: "Jim Shannon", id:"Jim_Shannon", x: .3761, r: .45, colour: "DUP", vocabulary: "3761", party: "DUP"},{MP: "Eleanor Laing", id:"Eleanor_Laing", x: .2081, r: .45, colour: "Conservative", vocabulary: "2081", party: "Conservative"},{MP: "Nick Smith", id:"Nick_Smith", x: .709, r: .45, colour: "Labour", vocabulary: "709", party: "Labour"},{MP: "John Mann", id:"John_Mann", x: .1464, r: .45, colour: "Labour", vocabulary: "1464", party: "Labour"},{MP: "John Spellar", id:"John_Spellar", x: .752, r: .45, colour: "Labour", vocabulary: "752", party: "Labour"},{MP: "Melanie Onn", id:"Melanie_Onn", x: .1608, r: .45, colour: "Labour", vocabulary: "1608", party: "Labour"},{MP: "Derek Twigg", id:"Derek_Twigg", x: .950, r: .45, colour: "Labour", vocabulary: "950", party: "Labour"},{MP: "Anna Soubry", id:"Anna_Soubry", x: .2432, r: .45, colour: "Conservative", vocabulary: "2432", party: "Conservative"},{MP: "Charles Walker", id:"Charles_Walker", x: .1232, r: .45, colour: "Conservative", vocabulary: "1232", party: "Conservative"},{MP: "Ruth Cadbury", id:"Ruth_Cadbury", x: .1872, r: .45, colour: "Labour", vocabulary: "1872", party: "Labour"},{MP: "Meg Hillier", id:"Meg_Hillier", x: .851, r: .45, colour: "Labour", vocabulary: "851", party: "Labour"},{MP: "Julian Knight", id:"Julian_Knight", x: .1933, r: .45, colour: "Conservative", vocabulary: "1933", party: "Conservative"},{MP: "Steve Brine", id:"Steve_Brine", x: .1423, r: .45, colour: "Conservative", vocabulary: "1423", party: "Conservative"},{MP: "James Gray", id:"James_Gray", x: .1255, r: .45, colour: "Conservative", vocabulary: "1255", party: "Conservative"},{MP: "Liam Byrne", id:"Liam_Byrne", x: .1139, r: .45, colour: "Labour", vocabulary: "1139", party: "Labour"},{MP: "John Stevenson", id:"John_Stevenson", x: .782, r: .45, colour: "Conservative", vocabulary: "782", party: "Conservative"},{MP: "Paul Monaghan", id:"Paul_Monaghan", x: .810, r: .45, colour: "Scottish National Party", vocabulary: "810", party: "Scottish National Party"},{MP: "Maria Eagle", id:"Maria_Eagle", x: .1226, r: .45, colour: "Labour", vocabulary: "1226", party: "Labour"},{MP: "Zac Goldsmith", id:"Zac_Goldsmith", x: .792, r: .45, colour: "Conservative", vocabulary: "792", party: "Conservative"},{MP: "Kirsten Oswald", id:"Kirsten_Oswald", x: .1817, r: .45, colour: "Scottish National Party", vocabulary: "1817", party: "Scottish National Party"},{MP: "Lucy Powell", id:"Lucy_Powell", x: .1573, r: .45, colour: "Labour", vocabulary: "1573", party: "Labour"},{MP: "Greg Clark", id:"Greg_Clark", x: .2897, r: .45, colour: "Conservative", vocabulary: "2897", party: "Conservative"},{MP: "Michael Fallon", id:"Michael_Fallon", x: .2839, r: .45, colour: "Conservative", vocabulary: "2839", party: "Conservative"},{MP: "Nigel Dodds", id:"Nigel_Dodds", x: .2084, r: .45, colour: "DUP", vocabulary: "2084", party: "DUP"},{MP: "Tania Mathias", id:"Tania_Mathias", x: .1682, r: .45, colour: "Conservative", vocabulary: "1682", party: "Conservative"},{MP: "Albert Owen", id:"Albert_Owen", x: .988, r: .45, colour: "Labour", vocabulary: "988", party: "Labour"},{MP: "Tasmina Ahmed-Sheikh", id:"Tasmina_Ahmed-Sheikh", x: .2339, r: .45, colour: "Scottish National Party", vocabulary: "2339", party: "Scottish National Party"},{MP: "Lyn Brown", id:"Lyn_Brown", x: .1606, r: .45, colour: "Labour", vocabulary: "1606", party: "Labour"},{MP: "David Nuttall", id:"David_Nuttall", x: .2708, r: .45, colour: "Conservative", vocabulary: "2708", party: "Conservative"},{MP: "Karen Buck", id:"Karen_Buck", x: .1872, r: .45, colour: "Labour", vocabulary: "1872", party: "Labour"},{MP: "Chi Onwurah", id:"Chi_Onwurah", x: .1428, r: .45, colour: "Labour", vocabulary: "1428", party: "Labour"},{MP: "Callum McCaig", id:"Callum_McCaig", x: .1722, r: .45, colour: "Scottish National Party", vocabulary: "1722", party: "Scottish National Party"},{MP: "Andrew Slaughter", id:"Andrew_Slaughter", x: .3076, r: .45, colour: "Labour", vocabulary: "3076", party: "Labour"},{MP: "Tobias Ellwood", id:"Tobias_Ellwood", x: .2451, r: .45, colour: "Conservative", vocabulary: "2451", party: "Conservative"},{MP: "Roger Mullin", id:"Roger_Mullin", x: .2229, r: .45, colour: "Scottish National Party", vocabulary: "2229", party: "Scottish National Party"},{MP: "Andrew Bingham", id:"Andrew_Bingham", x: .884, r: .45, colour: "Conservative", vocabulary: "884", party: "Conservative"},{MP: "Rebecca Harris", id:"Rebecca_Harris", x: .923, r: .45, colour: "Conservative", vocabulary: "923", party: "Conservative"},{MP: "Caroline Flint", id:"Caroline_Flint", x: .1790, r: .45, colour: "Labour", vocabulary: "1790", party: "Labour"},{MP: "Jamie Reed", id:"Jamie_Reed", x: .1823, r: .45, colour: "Labour", vocabulary: "1823", party: "Labour"},{MP: "Chris Heaton-Harris", id:"Chris_Heaton-Harris", x: .1844, r: .45, colour: "Conservative", vocabulary: "1844", party: "Conservative"},{MP: "Harriet Harman", id:"Harriet_Harman", x: .2181, r: .45, colour: "Labour", vocabulary: "2181", party: "Labour"},{MP: "Bill Cash", id:"Bill_Cash", x: .3240, r: .45, colour: "Conservative", vocabulary: "3240", party: "Conservative"},{MP: "Gerald Jones", id:"Gerald_Jones", x: .931, r: .45, colour: "Labour", vocabulary: "931", party: "Labour"},{MP: "Jo Cox", id:"Jo_Cox", x: .1619, r: .45, colour: "Labour", vocabulary: "1619", party: "Labour"},{MP: "Alasdair McDonnell", id:"Alasdair_McDonnell", x: .1079, r: .45, colour: "Social Democratic and Labour Party", vocabulary: "1079", party: "Social Democratic and Labour Party"},{MP: "Tom Watson", id:"Tom_Watson", x: .288, r: .45, colour: "Labour", vocabulary: "288", party: "Labour"},{MP: "Nigel Evans", id:"Nigel_Evans", x: .1421, r: .45, colour: "Conservative", vocabulary: "1421", party: "Conservative"},{MP: "Gregory Campbell", id:"Gregory_Campbell", x: .460, r: .45, colour: "DUP", vocabulary: "460", party: "DUP"},{MP: "Stephen Phillips", id:"Stephen_Phillips", x: .2917, r: .45, colour: "Conservative", vocabulary: "2917", party: "Conservative"},{MP: "Rory Stewart", id:"Rory_Stewart", x: .1857, r: .45, colour: "Conservative", vocabulary: "1857", party: "Conservative"},{MP: "Ian Lavery", id:"Ian_Lavery", x: .1611, r: .45, colour: "Labour", vocabulary: "1611", party: "Labour"},{MP: "Keith Vaz", id:"Keith_Vaz", x: .2267, r: .45, colour: "Labour", vocabulary: "2267", party: "Labour"},{MP: "Jo Johnson", id:"Jo_Johnson", x: .1077, r: .45, colour: "Conservative", vocabulary: "1077", party: "Conservative"},{MP: "Valerie Vaz", id:"Valerie_Vaz", x: .1978, r: .45, colour: "Labour", vocabulary: "1978", party: "Labour"},{MP: "Ronnie Cowan", id:"Ronnie_Cowan", x: .1599, r: .45, colour: "Scottish National Party", vocabulary: "1599", party: "Scottish National Party"},{MP: "Oliver Dowden", id:"Oliver_Dowden", x: .1157, r: .45, colour: "Conservative", vocabulary: "1157", party: "Conservative"},{MP: "Byron Davies", id:"Byron_Davies", x: .1438, r: .45, colour: "Conservative", vocabulary: "1438", party: "Conservative"},{MP: "Stephen Gethins", id:"Stephen_Gethins", x: .1455, r: .45, colour: "Scottish National Party", vocabulary: "1455", party: "Scottish National Party"},{MP: "Mary Glindon", id:"Mary_Glindon", x: .413, r: .45, colour: "Labour", vocabulary: "413", party: "Labour"},{MP: "Seema Kennedy", id:"Seema_Kennedy", x: .1647, r: .45, colour: "Conservative", vocabulary: "1647", party: "Conservative"},{MP: "Maggie Throup", id:"Maggie_Throup", x: .1809, r: .45, colour: "Conservative", vocabulary: "1809", party: "Conservative"},{MP: "Shailesh Vara", id:"Shailesh_Vara", x: .1698, r: .45, colour: "Conservative", vocabulary: "1698", party: "Conservative"},{MP: "Michelle Thomson", id:"Michelle_Thomson", x: .1451, r: .45, colour: "Scottish National Party", vocabulary: "1451", party: "Scottish National Party"},{MP: "David Morris", id:"David_Morris", x: .998, r: .45, colour: "Conservative", vocabulary: "998", party: "Conservative"},{MP: "Richard Drax", id:"Richard_Drax", x: .1786, r: .45, colour: "Conservative", vocabulary: "1786", party: "Conservative"},{MP: "James Berry", id:"James_Berry", x: .1767, r: .45, colour: "Conservative", vocabulary: "1767", party: "Conservative"},{MP: "Peter Bone", id:"Peter_Bone", x: .1791, r: .45, colour: "Conservative", vocabulary: "1791", party: "Conservative"},{MP: "Douglas 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McLaughlin", id:"Anne_McLaughlin", x: .2412, r: .45, colour: "Scottish National Party", vocabulary: "2412", party: "Scottish National Party"},{MP: "Nadhim Zahawi", id:"Nadhim_Zahawi", x: .1010, r: .45, colour: "Conservative", vocabulary: "1010", party: "Conservative"},{MP: "Victoria Prentis", id:"Victoria_Prentis", x: .1039, r: .45, colour: "Conservative", vocabulary: "1039", party: "Conservative"},{MP: "Simon Hoare", id:"Simon_Hoare", x: .2744, r: .45, colour: "Conservative", vocabulary: "2744", party: "Conservative"},{MP: "Ivan Lewis", id:"Ivan_Lewis", x: .678, r: .45, colour: "Labour", vocabulary: "678", party: "Labour"},{MP: "Cat Smith", id:"Cat_Smith", x: .1934, r: .45, colour: "Labour", vocabulary: "1934", party: "Labour"},{MP: "Richard Burden", id:"Richard_Burden", x: .1198, r: .45, colour: "Labour", vocabulary: "1198", party: "Labour"},{MP: "Dennis Skinner", id:"Dennis_Skinner", x: .947, r: .45, colour: "Labour", vocabulary: "947", party: "Labour"},{MP: "Liz Kendall", id:"Liz_Kendall", x: .514, r: .45, colour: "Labour", vocabulary: "514", party: "Labour"},{MP: "Steven Baker", id:"Steven_Baker", x: .1521, r: .45, colour: "Conservative", vocabulary: "1521", party: "Conservative"},{MP: "Helen Goodman", id:"Helen_Goodman", x: .2311, r: .45, colour: "Labour", vocabulary: "2311", party: "Labour"},{MP: "Nusrat Ghani", id:"Nusrat_Ghani", x: .1263, r: .45, colour: "Conservative", vocabulary: "1263", party: "Conservative"},{MP: "Joan Ryan", id:"Joan_Ryan", x: .1578, r: .45, colour: "Labour", vocabulary: "1578", party: "Labour"},{MP: "Alan Campbell", id:"Alan_Campbell", x: .104, r: .45, colour: "Labour", vocabulary: "104", party: "Labour"},{MP: "John Whittingdale", id:"John_Whittingdale", x: .2602, r: .45, colour: "Conservative", vocabulary: "2602", party: "Conservative"},{MP: "John Pugh", id:"John_Pugh", x: .1185, r: .45, colour: "Liberal Democrat", vocabulary: "1185", party: "Liberal Democrat"},{MP: "Kerry McCarthy", id:"Kerry_McCarthy", x: .1864, r: .45, colour: "Labour", vocabulary: "1864", party: "Labour"},{MP: "Bob Stewart", id:"Bob_Stewart", x: .1530, r: .45, colour: "Conservative", vocabulary: "1530", party: "Conservative"},{MP: "Adam Holloway", id:"Adam_Holloway", x: .862, r: .45, colour: "Conservative", vocabulary: "862", party: "Conservative"},{MP: "David Mowat", id:"David_Mowat", x: .1421, r: .45, colour: "Conservative", vocabulary: "1421", party: "Conservative"},{MP: "Jessica Morden", id:"Jessica_Morden", x: .615, r: .45, colour: "Labour", vocabulary: "615", party: "Labour"},{MP: "Jane Ellison", id:"Jane_Ellison", x: .2269, r: .45, colour: "Conservative", vocabulary: "2269", party: "Conservative"},{MP: "Matthew Offord", id:"Matthew_Offord", x: .1227, r: .45, colour: "Conservative", vocabulary: "1227", party: "Conservative"},{MP: "Stephen Hammond", id:"Stephen_Hammond", x: .1506, r: .45, colour: "Conservative", vocabulary: "1506", party: "Conservative"},{MP: "Julian Sturdy", id:"Julian_Sturdy", x: .966, r: .45, colour: "Conservative", vocabulary: "966", party: "Conservative"},{MP: "Rushanara Ali", id:"Rushanara_Ali", x: .292, r: .45, colour: "Labour", vocabulary: "292", party: "Labour"},{MP: "Adam Afriyie", id:"Adam_Afriyie", x: .418, r: .45, colour: "Conservative", vocabulary: "418", party: "Conservative"},{MP: "George Eustice", id:"George_Eustice", x: .1078, r: .45, colour: "Conservative", vocabulary: "1078", party: "Conservative"},{MP: "Alan Whitehead", id:"Alan_Whitehead", x: .603, r: .45, colour: "Labour", vocabulary: "603", party: "Labour"},{MP: "David Lidington", id:"David_Lidington", x: .2721, r: .45, colour: "Conservative", vocabulary: "2721", party: "Conservative"},{MP: "Owen Paterson", id:"Owen_Paterson", x: .1504, r: .45, colour: "Conservative", vocabulary: "1504", party: "Conservative"},{MP: "Damian Green", id:"Damian_Green", x: .1615, r: .45, colour: "Conservative", vocabulary: "1615", party: "Conservative"},{MP: "Heather Wheeler", id:"Heather_Wheeler", x: .609, r: .45, colour: "Conservative", vocabulary: "609", party: "Conservative"},{MP: "Shabana Mahmood", id:"Shabana_Mahmood", x: .1843, r: .45, colour: "Labour", vocabulary: "1843", party: "Labour"},{MP: "Guy Opperman", id:"Guy_Opperman", x: .11, r: .45, colour: "Conservative", vocabulary: "11", party: "Conservative"},{MP: "Kevan Jones", id:"Kevan_Jones", x: .2447, r: .45, colour: "Labour", vocabulary: "2447", party: "Labour"},{MP: "George Howarth", id:"George_Howarth", x: .986, r: .45, colour: "Labour", vocabulary: "986", party: "Labour"},{MP: "David Hanson", id:"David_Hanson", x: .1405, r: .45, colour: "Labour", vocabulary: "1405", party: "Labour"},{MP: "Jonathan Reynolds", id:"Jonathan_Reynolds", x: .2201, r: .45, colour: "Labour", vocabulary: "2201", party: "Labour"},{MP: "Nia Griffith", id:"Nia_Griffith", x: .1163, r: .45, colour: "Labour", vocabulary: "1163", party: "Labour"},{MP: "Robert Syms", id:"Robert_Syms", x: .718, r: .45, colour: "Conservative", vocabulary: "718", party: "Conservative"},{MP: "David Davis", id:"David_Davis", x: .2096, r: .45, colour: "Conservative", vocabulary: "2096", party: "Conservative"},{MP: "Alan Duncan", id:"Alan_Duncan", x: .852, r: .45, colour: "Conservative", vocabulary: "852", party: "Conservative"},{MP: "Steve Reed", id:"Steve_Reed", x: .1913, r: .45, colour: "Labour", vocabulary: "1913", party: "Labour"},{MP: "Kirsty Blackman", id:"Kirsty_Blackman", x: .1221, r: .45, colour: "Scottish National Party", vocabulary: "1221", party: "Scottish National Party"},{MP: "David Lammy", id:"David_Lammy", x: .1924, r: .45, colour: "Labour", vocabulary: "1924", party: "Labour"},{MP: "Graham Evans", id:"Graham_Evans", x: .1754, r: .45, colour: "Conservative", vocabulary: "1754", party: "Conservative"},{MP: "Mark Field", id:"Mark_Field", x: .1569, r: .45, colour: "Conservative", vocabulary: "1569", party: "Conservative"},{MP: "Marie Rimmer", id:"Marie_Rimmer", x: .1653, r: .45, colour: "Labour", vocabulary: "1653", party: "Labour"},{MP: "Tristram Hunt", id:"Tristram_Hunt", x: .2115, r: .45, colour: "Labour", vocabulary: "2115", party: "Labour"},{MP: "George Osborne", id:"George_Osborne", x: .4379, r: .45, colour: "Conservative", vocabulary: "4379", party: "Conservative"},{MP: "Justine Greening", id:"Justine_Greening", x: .2048, r: .45, colour: "Conservative", vocabulary: "2048", party: "Conservative"},{MP: "Kate Hollern", id:"Kate_Hollern", x: .617, r: .45, colour: "Labour", vocabulary: "617", party: "Labour"},{MP: "David Gauke", id:"David_Gauke", x: .3061, r: .45, colour: "Conservative", vocabulary: "3061", party: "Conservative"},{MP: "Joanna Cherry", id:"Joanna_Cherry", x: .2576, r: .45, colour: "Scottish National Party", vocabulary: "2576", party: "Scottish National Party"},{MP: "Steve Rotheram", id:"Steve_Rotheram", x: .138, r: .45, colour: "Labour", vocabulary: "138", party: "Labour"},{MP: "Nick Thomas-Symonds", id:"Nick_Thomas-Symonds", x: .1554, r: .45, colour: "Labour", vocabulary: "1554", party: "Labour"},{MP: "Jon Trickett", id:"Jon_Trickett", x: .1378, r: .45, colour: "Labour", vocabulary: "1378", party: "Labour"},{MP: "Ed Miliband", id:"Ed_Miliband", x: .1823, r: .45, colour: "Labour", vocabulary: "1823", party: "Labour"},{MP: "David Amess", id:"David_Amess", x: .2049, r: .45, colour: "Conservative", vocabulary: "2049", party: "Conservative"},{MP: "Stephen Philip Rotheram", id:"Stephen_Philip_Rotheram", x: .600, r: .45, colour: "Labour", vocabulary: "600", party: "Labour"},{MP: "Ben Gummer", id:"Ben_Gummer", x: .2901, r: .45, colour: "Conservative", vocabulary: "2901", party: "Conservative"},{MP: "Daniel Kawczynski", id:"Daniel_Kawczynski", x: .673, r: .45, colour: "Conservative", vocabulary: "673", party: "Conservative"},{MP: "Alan Brown", id:"Alan_Brown", x: .2130, r: .45, colour: "Scottish National Party", vocabulary: "2130", party: "Scottish National Party"},{MP: "Diane Abbott", id:"Diane_Abbott", x: .716, r: .45, colour: "Labour", vocabulary: "716", party: "Labour"},{MP: "Geoffrey Cox", id:"Geoffrey_Cox", x: .66, r: .45, colour: "Conservative", vocabulary: "66", party: "Conservative"},{MP: "Mark Tami", id:"Mark_Tami", x: .669, r: .45, colour: "Labour", vocabulary: "669", party: "Labour"},{MP: "Philippa Whitford", id:"Philippa_Whitford", x: .2231, r: .45, colour: "Scottish National Party", vocabulary: "2231", party: "Scottish National Party"},{MP: "Caroline Ansell", id:"Caroline_Ansell", x: .988, r: .45, colour: "Conservative", vocabulary: "988", party: "Conservative"},{MP: "Jeremy Wright", id:"Jeremy_Wright", x: .931, r: .45, colour: "Conservative", vocabulary: "931", party: "Conservative"},{MP: "Therese Coffey", id:"Therese_Coffey", x: .1817, r: .45, colour: "Conservative", vocabulary: "1817", party: "Conservative"},{MP: "Wayne David", id:"Wayne_David", x: .1913, r: .45, colour: "Labour", vocabulary: "1913", party: "Labour"},{MP: "Lisa Cameron", id:"Lisa_Cameron", x: .2607, r: .45, colour: "Scottish National Party", vocabulary: "2607", party: "Scottish National Party"},{MP: "Greg Hands", id:"Greg_Hands", x: .1544, r: .45, colour: "Conservative", vocabulary: "1544", party: "Conservative"},{MP: "Rebecca Pow", id:"Rebecca_Pow", x: .1903, r: .45, colour: "Conservative", vocabulary: "1903", party: "Conservative"},{MP: "Dominic Raab", id:"Dominic_Raab", x: .658, r: .45, colour: "Conservative", vocabulary: "658", party: "Conservative"},{MP: "John Redwood", id:"John_Redwood", x: .2901, r: .45, colour: "Conservative", vocabulary: "2901", party: "Conservative"},{MP: "Keith Simpson", id:"Keith_Simpson", x: .459, r: .45, colour: "Conservative", vocabulary: "459", party: "Conservative"},{MP: "Madeleine Moon", id:"Madeleine_Moon", x: .1548, r: .45, colour: "Labour", vocabulary: "1548", party: "Labour"},{MP: "Harriett Baldwin", id:"Harriett_Baldwin", x: .2394, r: .45, colour: "Conservative", vocabulary: "2394", party: "Conservative"},{MP: "Rishi Sunak", id:"Rishi_Sunak", x: .1188, r: .45, colour: "Conservative", vocabulary: "1188", party: "Conservative"},{MP: "Chris Matheson", id:"Chris_Matheson", x: .1703, r: .45, colour: "Labour", vocabulary: "1703", party: "Labour"},{MP: "Marcus Jones", id:"Marcus_Jones", x: .1156, r: .45, colour: "Conservative", vocabulary: "1156", party: "Conservative"},{MP: "Suella Fernandes", id:"Suella_Fernandes", x: .2275, r: .45, colour: "Conservative", vocabulary: "2275", party: "Conservative"},{MP: "James Cleverly", id:"James_Cleverly", x: .1439, r: .45, colour: "Conservative", vocabulary: "1439", party: "Conservative"},{MP: "Alistair Burt", id:"Alistair_Burt", x: .3352, r: .45, colour: "Conservative", vocabulary: "3352", party: "Conservative"},{MP: "David Anderson", id:"David_Anderson", x: .1525, r: .45, colour: "Labour", vocabulary: "1525", party: "Labour"},{MP: "Kelly Tolhurst", id:"Kelly_Tolhurst", x: .1155, r: .45, colour: "Conservative", vocabulary: "1155", party: "Conservative"},{MP: "Oliver Letwin", id:"Oliver_Letwin", x: .445, r: .45, colour: "Conservative", vocabulary: "445", party: "Conservative"}'
In [9]:
# Initialise party talk dict with each party name
party_talk = {}
for party in parties.keys():
party_talk.update({party:''})
for speaker, party_and_text in words_spoken_by_mp.items():
# will throw error if party doesn't exist in parties list
party, text = party_and_text
if len(party_talk[party]) == 0:
party_talk[party] = text
else:
party_talk[party] = party_talk[party] + ' ' + text
In [12]:
con_sentences = nltk.tokenize.sent_tokenize(party_talk['Conservative'])
con_sentences = ["{0} {1} {2}".format("START_TOKEN", w, "END_TOKEN") for w in con_sentences]
con_sentences = [nltk.word_tokenize(sentence) for sentence in con_sentences]
In [14]:
Out[14]:
['START_TOKEN',
'Where',
'possible',
',',
'we',
'are',
'looking',
'at',
'alternative',
'solutions',
'and',
',',
'of',
'course',
',',
'we',
'are',
'bringing',
'forward',
'carbon',
'capture',
'and',
'storage',
'as',
'a',
'long-term',
'solution',
'.',
'END_TOKEN']
In [16]:
word_freqs = nltk.FreqDist(con_sentences)
vocab = word_freqs.most_common(9999)
idx_2_word = [x[0] for x in vocab]
idx_2_word.append("TOO_INFREQUENT_WORD")
word_2_idx = dict([(w,i) for i,w in enumerate(idx_2_word)])
for i, sentence in enumerate(con_sentences):
con_sentences[i] = [word if word in word_2_idx else "TOO_INFREQUENT_WORD" for word in sentence]
X_train = np.asarray([[word_2_idx[w] for w in sent[:-1]] for sent in con_sentences])
Y_train = np.asarray([[word_2_idx[w] for w in sent[1:]] for sent in con_sentences])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-16-d2a75efe188d> in <module>()
----> 1 word_freqs = nltk.FreqDist(*con_sentences)
2 vocab = word_freqs.most_common(9999)
3
4 idx_2_word = [x[0] for x in vocab]
5 idx_2_word.append("TOO_INFREQUENT_WORD")
TypeError: __init__() takes from 1 to 2 positional arguments but 147486 were given
In [ ]:
Using the raw data discover the following:
Then apply NLTK methods and look at vocabulary:
Other ideas
In [13]:
total_speech = []
for speaker, party_text in words_spoken_by_mp.items():
total_speech.append((speaker, party_text[0], len(party_text[1])))
In [14]:
average_totals = []
for party, mp_number in parties.items():
average_totals.append((party, len(party_talk[party])/mp_number))
average_totals = sorted(average_totals, key=lambda x: x[1], reverse=True)
for party, average in average_totals:
print("{0:<40s}: {1:>10,.1f}".format(party, average))
Green : 108,574.0
Social Democratic and Labour Party : 91,118.3
DUP : 64,063.9
Scottish National Party : 57,385.4
Liberal Democrat : 55,469.6
Conservative : 52,366.0
UUP : 43,351.5
Plaid Cymru : 42,344.0
Labour : 40,961.8
Independent : 26,885.0
UKIP : 25,777.0
Sinn Féin : 0.0
In [19]:
y_pos = np.arange(len(average_totals))
plt.figure(figsize=(10,10))
plot_mp_numbers = [parties[x[1]] for x in average_totals]
plt.bar(y_pos, [x[1] for x in average_totals], align='center', alpha=0.4)
plt.xticks(y_pos, [x[0] for x in average_totals], rotation=90)
plt.ylabel('Average words spoken per MP')
plt.xlabel('Party')
plt.show()
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-19-ee7a85864047> in <module>()
1 y_pos = np.arange(len(average_totals))
2 plt.figure(figsize=(10,10))
----> 3 plot_mp_numbers = [parties[x[1]] for x in average_totals]
4 plt.bar(y_pos, [x[1] for x in average_totals], align='center', alpha=0.4)
5 plt.xticks(y_pos, [x[0] for x in average_totals], rotation=90)
<ipython-input-19-ee7a85864047> in <listcomp>(.0)
1 y_pos = np.arange(len(average_totals))
2 plt.figure(figsize=(10,10))
----> 3 plot_mp_numbers = [parties[x[1]] for x in average_totals]
4 plt.bar(y_pos, [x[1] for x in average_totals], align='center', alpha=0.4)
5 plt.xticks(y_pos, [x[0] for x in average_totals], rotation=90)
KeyError: 108574.0
<matplotlib.figure.Figure at 0x7f031c1560f0>
In [16]:
def make_wordcloud(text, image_filename=None):
"""
Generate a wordcloud
"""
# remove minor words
wordcloud.STOPWORDS.add("hon")
wordcloud.STOPWORDS.add("Government")
wordcloud.STOPWORDS.add("government")
wordcloud.STOPWORDS.add("Minister")
wordcloud.STOPWORDS.add("minister")
wordcloud.STOPWORDS.add("S")
wordcloud.STOPWORDS.add("s")
wordcloud.STOPWORDS.add("Member")
wordcloud.STOPWORDS.add("Friend")
if image_filename:
mask = imread(image_filename)
wc = wordcloud.WordCloud(background_color="white", max_words=2000, mask=mask,
stopwords=wordcloud.STOPWORDS)
else:
wc = wordcloud.WordCloud(background_color="white", max_words=2000, stopwords=wordcloud.STOPWORDS)
wc.generate(text)
return wc
In [17]:
plt.figure(figsize=(20,10))
plt.subplot(241)
plt.imshow(make_wordcloud(party_talk['Conservative']))
plt.axis("off")
plt.title('Conservative')
plt.subplot(242)
plt.imshow(make_wordcloud(party_talk['Labour']))
plt.axis("off")
plt.title('Labour')
plt.subplot(243)
plt.imshow(make_wordcloud(party_talk['Scottish National Party']))
plt.axis("off")
plt.title('SNP')
plt.subplot(244)
plt.imshow(make_wordcloud(party_talk['Liberal Democrat']))
plt.axis("off")
plt.title('Lib Dem')
plt.subplot(245)
plt.imshow(make_wordcloud(party_talk['DUP']))
plt.axis("off")
plt.title('DUP')
plt.subplot(246)
plt.imshow(make_wordcloud(party_talk['Green']))
plt.axis("off")
plt.title('Green')
plt.subplot(247)
plt.imshow(make_wordcloud(party_talk['UKIP']))
plt.axis("off")
plt.title('UKIP')
plt.subplot(248)
plt.imshow(make_wordcloud(party_talk['Plaid Cymru']))
plt.axis("off")
plt.title('PC')
/usr/lib/python3.5/site-packages/PIL/ImageDraw.py:104: UserWarning: setfont() is deprecated. Please set the attribute directly instead.
"Please set the attribute directly instead.")
Out[17]:
<matplotlib.text.Text at 0x7f030fc4f7b8>
Generate data for d3 plot of total spoken words per MP (based on http://experiments.undercurrent.com/)
Format data = {MP: NAME,
x: 0.WORDS,
r: .3,
color: PARTY,
words: "WORDS",
id:NAME,
party: PARTY
cx:
cy:
In [18]:
Out[18]:
5
In [ ]:
Content source: fmaguire/hansard_mining
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