#GE2020 Analysing Party Manifestos using Python
The general election is underway here in Ireland with polling day set for Saturday 8th February. All the politicians are out campaigning and every day the various parties are looking for publicity on whatever the popular topic is for that day. Each day is it a different topic.
Most of the political parties have not released their manifestos for the #GE2020 election (as of date of this post). I want to use some simple Python code to perform some analyse of their manifestos. As their new manifestos weren’t available (yet) I went looking for their manifestos from the previous general election. Michael Pidgeon has a website with party manifestos dating back to the early 1970s, and also has some from earlier elections. Check out his website.
I decided to look at manifestos from the 4 main political parties from the 2016 general election. Yes there are other manifestos available, and you can use the Python code, given below to analyse those, with only some minor edits required.
The end result of this simple analyse is a WordCloud showing the most commonly used words in their manifestos. This is graphical way to see what some of the main themes and emphasis are for each party, and also allows us to see some commonality between the parties.
Let’s begin with the Python code.
1 – Initial Setup
There are a number of Python Libraries available for processing PDF files. Not all of them worked on all of the Part Manifestos PDFs! It kind of depends on how these files were generated. In my case I used the pdfminer library, as it worked with all four manifestos. The common library PyPDF2 didn’t work with the Fine Gael manifesto document.
import io import pdfminer from pprint import pprint from pdfminer.converter import TextConverter from pdfminer.pdfinterp import PDFPageInterpreter from pdfminer.pdfinterp import PDFResourceManager from pdfminer.pdfpage import PDFPage #directory were manifestos are located wkDir = '.../General_Election_Ire/' #define the names of the Manifesto PDF files & setup party flag pdfFile = wkDir+'FGManifesto16_2.pdf' party = 'FG' #pdfFile = wkDir+'Fianna_Fail_GE_2016.pdf' #party = 'FF' #pdfFile = wkDir+'Labour_GE_2016.pdf' #party = 'LB' #pdfFile = wkDir+'Sinn_Fein_GE_2016.pdf' #party = 'SF'
All of the following code will run for a given manifesto. Just comment in or out the manifesto you are interested in. The WordClouds for each are given below.
2 – Load the PDF File into Python
The following code loops through each page in the PDF file and extracts the text from that page.
I added some addition code to ignore pages containing the Irish Language. The Sinn Fein Manifesto contained a number of pages which were the Irish equivalent of the preceding pages in English. I didn’t want to have a mixture of languages in the final output.
SF_IrishPages = [14,15,16,17,18,19,20,21,22,23,24]
text = ""
pageCounter = 0
resource_manager = PDFResourceManager()
fake_file_handle = io.StringIO()
converter = TextConverter(resource_manager, fake_file_handle)
page_interpreter = PDFPageInterpreter(resource_manager, converter)
for page in PDFPage.get_pages(open(pdfFile,'rb'), caching=True, check_extractable=True):
if (party == 'SF') and (pageCounter in SF_IrishPages):
print(party+' - Not extracting page - Irish page', pageCounter)
else:
print(party+' - Extracting Page text', pageCounter)
page_interpreter.process_page(page)
text = fake_file_handle.getvalue()
pageCounter += 1
print('Finished processing PDF document')
converter.close()
fake_file_handle.close()
FG - Extracting Page text 0 FG - Extracting Page text 1 FG - Extracting Page text 2 FG - Extracting Page text 3 FG - Extracting Page text 4 FG - Extracting Page text 5 ...
3 – Tokenize the Words
The next step is to Tokenize the text. This breaks the text into individual words.
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
tokens = []
tokens = word_tokenize(text)
print('Number of Pages =', pageCounter)
print('Number of Tokens =',len(tokens))
Number of Pages = 140 Number of Tokens = 66975
4 – Filter words, Remove Numbers & Punctuation
There will be a lot of things in the text that we don’t want included in the analyse. We want the text to only contain words. The following extracts the words and ignores numbers, punctuation, etc.
#converts to lower case, and removes punctuation and numbers wordsFiltered = [tokens.lower() for tokens in tokens if tokens.isalpha()] print(len(wordsFiltered)) print(wordsFiltered)
58198 ['fine', 'gael', 'general', 'election', 'manifesto', 's', 'keep', 'the', 'recovery', 'going', 'gaelgeneral', 'election', 'manifesto', 'foreward', 'from', 'an', 'taoiseach', 'the', 'long', 'term', 'economic', 'three', 'steps', 'to', 'keep', 'the', 'recovery', 'going', 'agriculture', 'and', 'food', 'generational', ...
As you can see the number of tokens has reduced from 66,975 to 58,198.
5 – Setup Stop Words
Stop words are general words in a language that doesn’t contain any meanings and these can be removed from the data set. Python NLTK comes with a set of stop words defined for most languages.
#We initialize the stopwords variable which is a list of words like
#"The", "I", "and", etc. that don't hold much value as keywords
stop_words = stopwords.words('english')
print(stop_words)
['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', ....
Additional stop words can be added to this list. I added the words listed below. Some of these you might expect to be in the stop word list, others are to remove certain words that appeared in the various manifestos that don’t have a lot of meaning. I also added the name of the parties and some Irish words to the stop words list.
#some extra stop words are needed after examining the data and word cloud #these are added extra_stop_words = ['ireland','irish','ł','need', 'also', 'set', 'within', 'use', 'order', 'would', 'year', 'per', 'time', 'place', 'must', 'years', 'much', 'take','make','making','manifesto','ð','u','part','needs','next','keep','election', 'fine','gael', 'gaelgeneral', 'fianna', 'fáil','fail','labour', 'sinn', 'fein','féin','atá','go','le','ar','agus','na','ár','ag','haghaidh','téarnamh','bplean','page','two','number','cothromfor'] stop_words.extend(extra_stop_words) print(stop_words)
Now remove these stop words from the list of tokens.
# remove stop words from tokenised data set filtered_words = [word for word in wordsFiltered if word not in stop_words] print(len(filtered_words)) print(filtered_words)
31038 ['general', 'recovery', 'going', 'foreward', 'taoiseach', 'long', 'term', 'economic', 'three', 'steps', 'recovery', 'going', 'agriculture', 'food',
The number of tokens is reduced to 31,038
6 – Word Frequency Counts
Now calculate how frequently these words occur in the list of tokens.
#get the frequency of each word from collections import Counter # count frequencies cnt = Counter() for word in filtered_words: cnt[word] += 1 print(cnt)
Counter({'new': 340, 'support': 249, 'work': 190, 'public': 186, 'government': 177, 'ensure': 177, 'plan': 176, 'continue': 168, 'local': 150,
...
7 – WordCloud
We can use the word frequency counts to add emphasis to the WordCloud. The more frequently it occurs the larger it will appear in the WordCloud.
#create a word cloud using frequencies for emphasis
from wordcloud import WordCloud
import matplotlib.pyplot as plt
wc = WordCloud(max_words=100, margin=9, background_color='white',
scale=3, relative_scaling = 0.5, width=500, height=400,
random_state=1).generate_from_frequencies(cnt)
plt.figure(figsize=(20,10))
plt.imshow(wc)
#plt.axis("off")
plt.show()
#Save the image in the img folder:
wc.to_file(wkDir+party+"_2016.png")
The last line of code saves the WordCloud image as a file in the directory where the manifestos are located.
8 – WordClouds for Each Party
Remember these WordClouds are for the manifestos from the 2016 general election.
When the parties have released their manifestos for the 2020 general election, I’ll run them through this code and produce the WordClouds for 2020. It will be interesting to see the differences between the 2016 and 2020 manifesto WordClouds.
Ora-lytics 