Tracking Social Issues and Topics in Presidential Speeches

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Scraping presidential transcripts

To begin, we must scrape the content of all presidential speeches recorded in American history. To do that, I’ll rely on the very handy BeautifulSoup library, and eventually store all data in a pandas dataframe that will be persisted in a pickle file.

 
# import required libraries to scrape presidential transcripts
from bs4 import BeautifulSoup
import pandas as pd
import pickle
import urllib2
import re
 
def get_speech_links():
    '''
    scrape content of pages with all presidential transcript links
    '''
    home_url = 'http://millercenter.org/president/speeches'
    try:
        response = urllib2.urlopen(home_url)
        page_source = response.read()
        soup = BeautifulSoup(page_source, "html5lib")
        transcript_links = soup.findAll("a", {'class': 'transcript'})
        return transcript_links
    except urllib2.HTTPError:
        print 'Homepage not available!'
        return None
 
def get_transcript(speech_link):
    '''
    scrape title of speech, date of speech and full transcipt
    contained in the input speech_link URL
    '''
    speaking = speech_link.split('/')[2]
    new_link = base_url + str(speech_link)
    try:
        response = urllib2.urlopen(new_link)
        page_source = response.read()
        soup = BeautifulSoup(page_source, "html5lib")
        title = soup.find('title').text
        speech_date = title.split('(', 1)[1].split(')')[0]
        transcript = soup.find('div', {'id': 'transcript'}).text
        transcript = transcript.replace('\n', ' ').replace('\r', '').replace('\t', '')
        return {'speaker': speaking,
                'date': speech_date,
                'title': title,
                'transcript': transcript}
    except urllib2.HTTPError:
        print 'skipped ' + str(speech_link)
        return None
# iterate through all links and extract content
transcript_links = get_speech_links()
base_url = 'http://millercenter.org/'
transcript_dict = {}
for i, link in enumerate(transcript_links):
    if i % 100 == 0:
        print 'Scraped ' + str(i) + '/' + str(len(transcript_links)) + ' of links...'
    if link.has_attr('href'):
        transcript_data = get_transcript(link['href'])
        if transcript_data is not None:
            key = transcript_data['speaker'] + '|' + transcript_data['date']
            transcript_dict[key] = transcript_data
 
# dump dataframe to pickle object
df = pd.DataFrame.from_dict(transcript_dict, orient='index')
pickle.dump(df, open( "presidential_speeches.pickle", "wb" ))

Topic modeling and visualization

Now that the raw text of all presidential speeches in American history has been retrieved, we can proceed to light preprocessing before applying Latent Dirichlet Allocation.

 
%matplotlib inline  
from gensim.parsing.preprocessing import STOPWORDS
from gensim.utils import simple_preprocess
from gensim.models import TfidfModel, LsiModel
from gensim.models.ldamodel import LdaModel
from gensim import corpora
from gensim import matutils
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from collections import defaultdict
import pyLDAvis.gensim as gensimvis
import pyLDAvis
import pandas as pd
import numpy as np
import pickle
import lda

In the following 5 cells, we effectively tokenize and remove stopwords from each document (i.e. presidential speech), compute the frequency of each token, and filter out all those that appear less than 10 times in the entire corpus of presidential speeches. Note that I used an ad-hoc threshold of 10, but this should be a parameter that could be played around. Also, the amount of porcessing on each document is intentionally simplistic. Finally, we set up gensim-specific objects that include a dictionary mapping words to integer ids, and a corpus that simply counts the number of occurences of each distinct word, converts the word to its integer word id and returns the result as a sparse vector.

 
def tokenize(text):
    return [token for token in simple_preprocess(text) if token not in STOPWORDS]
 
df = pickle.load( open( "presidential_speeches.pickle", "rb" ) )
speeches = df.transcript
documents = speeches.tolist()
texts = [tokenize(document) for document in documents]
 
frequency = defaultdict(int)
for text in texts:
    for token in text:
        frequency[token] += 1
 
texts = [[token for token in text if frequency[token] > 10] for text in texts]
 
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

Finding the Optimum Number of Topics

Now that the data is ready, we can run a batch LDA (because of the small size of the dataset that we are working with) to discover the main topics in our document.

# fit LDA model
speeches_topics = LdaModel(corpus=corpus,
                           id2word=dictionary,
                           num_topics=20,
                           passes=10)
# print out first 10 topics
for i, topic in enumerate(speeches_topics.print_topics(10)):
    print '{} --- {}'.format(i, topic)
0 --- 0.025*states + 0.017*united + 0.017*shall + 0.014*state + 0.010*constitution + 0.009*president + 0.009*act + 0.009*congress + 0.008*laws + 0.007*law

1 — 0.026government + 0.007states + 0.007chilean + 0.007men + 0.006sailors + 0.006united + 0.005mr + 0.005german + 0.005police + 0.004vessels

2 — 0.012world + 0.011peace + 0.009people + 0.008america + 0.008freedom + 0.007soviet + 0.006united + 0.006new + 0.005states + 0.005nations

3 — 0.050president + 0.030mr + 0.024think + 0.008secretary + 0.008general + 0.008people + 0.007time + 0.007viet + 0.007going + 0.007nam

4 — 0.011government + 0.007people + 0.007business + 0.006country + 0.005economic + 0.005congress + 0.005world + 0.005federal + 0.005tax + 0.005public

5 — 0.006people + 0.004government + 0.004united + 0.004states + 0.003country + 0.003public + 0.003congress + 0.003question + 0.003going + 0.002time

6 — 0.013states + 0.012government + 0.009united + 0.008congress + 0.007public + 0.005country + 0.005great + 0.005year + 0.004general + 0.004people

7 — 0.011peace + 0.010vietnam + 0.010people + 0.009war + 0.009world + 0.008united + 0.007south + 0.007american + 0.007nations + 0.006states

8 — 0.009world + 0.009congress + 0.008new + 0.008year + 0.007america + 0.006people + 0.006energy + 0.006american + 0.006nation + 0.005government

9 — 0.014government + 0.012people + 0.007states + 0.007union + 0.007constitution + 0.006great + 0.006shall + 0.006men + 0.006country + 0.005free

The display of inferred topics shown above does not really lend itself very well to interpretation. Aside from the fact that you have to read through all the topics, most people will interpret the main themes of each topics differently. This hits right to the core of my mixed feelings towards topic modeling. To be given the ability and opportunity to infer topics from a large set of documents is truly amazing, but I have always personally felt (and maybe that is just me) that the ensuing display of information was lacking. Indeed, I have found that the output of typical topic modeling techniques does not lend itself very well to visualization and – in the case of presentations to the uninitiated – interpretation. However, I recently came across the LDAviz R library developed by Kenny Shirley and Carson Sievert, which to paraphrase their words is a D3.js interactive visualization that's designed help you interpret the topics in a topic model fit to a corpus of text using LDA. Here, we use the great Python extension port of the LDAviz R library, available on GitHub at the following URL https://github.com/bmabey/pyLDAvis. Two attractive features of pyLDAviz are its ability to help interpret the topics extracted from a fitted LDA model, but also the fact that it can be easily incorporated within an iPython notebook in nothing more than two lines of code!

 
vis_data = gensimvis.prepare(speeches_topics, corpus, dictionary)
pyLDAvis.display(vis_data)

var ldavis_el2981044999446561546706059_data = {"plot.opts": {"xlab": "PC1", "ylab": "PC2"}, "topic.order": [16, 9, 14, 6, 17, 11, 12, 13, 5, 10, 4, 8, 15, 1, 7, 18, 19, 20, 2, 3], "token.table": {"Topic": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 2, 12, 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 1, 2, 3, 4, 5, 7, 10, 12, 3, 19, 3, 4, 5, 6, 8, 9, 10, 12, 13, 18, 19, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 2, 4, 5, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 1, 2, 3, 4, 5, 6, 7, 8, 14, 16, 1, 2, 3, 4, 5, 11, 1, 2, 6, 7, 1, 9, 11, 1, 2, 4, 5, 7, 8, 11, 12, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 15, 19, 5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 19, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 1, 4, 5, 7, 9, 14, 15, 17, 3, 1, 2, 3, 4, 10, 19, 1, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1, 3, 4, 7, 9, 1, 2, 3, 12, 1, 4, 5, 6, 7, 1, 2, 1, 2, 4, 11, 12, 1, 2, 5, 7, 9, 11, 12, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 20, 3, 5, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 19, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 18, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 5, 12, 3, 1, 2, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 19, 2, 3, 6, 8, 19, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 18, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 18, 1, 2, 5, 6, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 18, 1, 2, 4, 5, 7, 10, 11, 4, 7, 10, 1, 2, 3, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 17, 18, 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 17, 18, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 16, 17, 18, 20, 3, 6, 8, 9, 8, 16, 3, 5, 8, 16, 1, 2, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 16, 17, 18, 19, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 16, 18, 1, 2, 3, 4, 6, 8, 10, 11, 12, 1, 2, 3, 4, 6, 8, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 4, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1, 2, 4, 9, 10, 15, 1, 2, 4, 7, 12, 3, 9, 14, 3, 6, 1, 2, 3, 4, 12, 1, 2, 3, 4, 1, 2, 4, 8, 10, 11, 13, 15, 1, 4, 7, 8, 3, 1, 2, 6, 16, 18, 1, 2, 4, 11, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 1, 2, 7, 11, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 1, 3, 5, 6, 7, 9, 14, 15, 1, 3, 4, 9, 1, 2, 3, 4, 5, 8, 9, 10, 15, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 15, 16, 18, 3, 4, 6, 8, 9, 19, 1, 2, 3, 4, 6, 8, 13, 14, 19, 1, 2, 3, 7, 1, 7, 9, 12, 1, 3, 4, 7, 9, 12, 18, 1, 2, 4, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 16, 1, 2, 3, 4, 5, 6, 8, 9, 10, 17, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 17, 18, 1, 3, 4, 5, 6, 7, 8, 9, 20, 3, 6, 8, 9, 16, 17, 19, 1, 2, 4, 12, 1, 3, 5, 6, 1, 2, 4, 18, 1, 5, 7, 8, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 17, 1, 4, 5, 6, 7, 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 14, 18, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1, 2, 7, 8, 9, 1, 2, 3, 1, 2, 3, 4, 6, 8, 11, 14, 15, 16, 18, 19, 1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 19, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 19, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 13, 14, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 15, 18, 3, 8, 9, 1, 2, 8, 10, 12, 19, 1, 2, 4, 12, 1, 2, 3, 4, 6, 8, 11, 1, 2, 4, 7, 10, 11, 12, 18, 1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 15, 3, 5, 6, 7, 8, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 19, 2, 3, 7, 8, 15, 16, 1, 2, 3, 4, 6, 8, 9, 10, 12, 13, 15, 16, 18, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 16, 17, 18, 19, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 1, 2, 3, 5, 1, 2, 3, 10, 1, 2, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 18, 1, 2, 8, 11, 12, 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 15, 3, 4, 1, 2, 5, 8, 10, 11, 3, 5, 6, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 19, 1, 4, 8, 3, 4, 8, 3, 8, 1, 2, 3, 6, 8, 9, 17, 3, 4, 8, 3, 6, 19, 3, 13, 1, 2, 3, 6, 17, 1, 2, 3, 8, 9, 12, 17, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1, 3, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 17, 18, 3, 4, 6, 9, 10, 13, 19, 1, 2, 4, 2, 3, 4, 5, 6, 9, 13, 14, 16, 18, 2, 3, 5, 14, 1, 3, 14, 1, 2, 3, 4, 5, 6, 7, 8, 13, 15, 17, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 16, 18, 20, 2, 3, 4, 5, 7, 3, 1, 3, 4, 5, 6, 8, 3, 4, 8, 19, 15, 1, 2, 3, 4, 6, 7, 8, 9, 10, 13, 17, 1, 3, 7, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 3, 4, 8, 10, 13, 17, 19, 1, 1, 2, 3, 4, 8, 12, 13, 1, 3, 4, 5, 7, 9, 12, 18, 1, 2, 6, 1, 2, 3, 4, 5, 7, 1, 2, 5, 10, 18, 1, 2, 3, 4, 6, 7, 8, 9, 13, 15, 18, 1, 2, 3, 4, 6, 3, 1, 5, 7, 3, 19, 1, 3, 4, 8, 13, 19, 6, 17, 1, 3, 4, 5, 6, 7, 8, 9, 13, 14, 16, 17, 19, 3, 4, 5, 13, 16, 1, 2, 3, 4, 6, 8, 10, 12, 19, 1, 2, 12, 1, 3, 4, 6, 8, 1, 3, 4, 6, 8, 10, 1, 3, 4, 8, 1, 2, 3, 6, 8, 9, 15, 3, 4, 6, 8, 9, 17, 3, 4, 8, 9, 16, 1, 3, 4, 10, 11, 1, 5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 18, 19, 1, 2, 4, 1, 3, 4, 5, 6, 7, 8, 9, 10, 18, 6, 7, 9, 13, 1, 9, 15, 3, 5, 3, 1, 2, 3, 4, 5, 6, 8, 9, 10, 13, 16, 1, 4, 5, 7, 6, 8, 1, 3, 7, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 1, 2, 3, 4, 5, 6, 7, 13, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1, 3, 4, 5, 6, 7, 8, 9, 13, 14, 16, 17, 18, 19, 4, 5, 13, 1, 3, 4, 8, 1, 2, 6, 8, 9, 4, 8, 9, 1, 2, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 18, 1, 2, 3, 4, 5, 7, 1, 1, 12, 3, 5, 13, 3, 8, 16, 17, 19, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 3, 4, 5, 6, 7, 8, 13, 19, 3, 4, 5, 8, 9, 13, 1, 3, 4, 10, 1, 2, 3, 4, 5, 6, 8, 9, 12, 16, 1, 3, 4, 7, 8, 9, 2, 4, 8, 19, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 15, 16, 18, 1, 3, 6, 8, 9, 10, 12, 3, 6, 8, 9, 10, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 13, 14, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 3, 4, 6, 8, 1, 2, 3, 4, 5, 13, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 1, 2, 3, 4, 7, 8, 10, 12, 1, 2, 14, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1, 2, 3, 8, 9, 18, 1, 2, 3, 4, 10, 11, 12, 15, 1, 2, 3, 1, 2, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 8, 10, 15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 1, 2, 4, 5, 7, 9, 10, 13, 14, 1, 2, 3, 4, 6, 7, 8, 9, 1, 2, 4, 5, 7, 8, 10, 11, 17, 1, 2, 6, 11, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 3, 4, 1, 3, 4, 7, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1, 3, 12, 1, 3, 8, 11, 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