NLP on the 2020 Presidental Debates

In [70]:
import requests
import pandas as pd
from bs4 import BeautifulSoup as bs

url = "https://www.rev.com/blog/transcripts/donald-trump-joe-biden-1st-presidential-debate-transcript-2020"

page = requests.get(url)
soup= bs(page.content, "html.parser")
content_block = soup.find("div", {"class": "fl-callout-text"})
targets = content_block.findAll('p')
targets
In [71]:
targets[0].get_text()
Out[71]:
'Chris Wallace: (01:20)\nGood evening from the Health Education Campus of Case Western Reserve University and the Cleveland Clinic. I’m Chris Wallace of Fox News and I welcome you to the first of the 2020 Presidential Debates between President Donald J. Trump and former Vice President Joe Biden. This debate is sponsored by the Commission on Presidential debates. The Commission has designed the format, six roughly 15 minute segments with two minute answers from each candidate to the first question, then open discussion for the rest of each segment. Both campaigns have agreed to these rules. For the record, I decided the topics and the questions in each topic. I can assure you none of the questions has been shared with the Commission or the two candidates.'

Turn everything into a dataframe

In [72]:
# THIS GOT ALMOST EVERYTHING
# transcript = []
# prev_time = ''
# for t in targets:
#     target = t.get_text()
#     try:
#         name = target.split(':')[0]
#         time = target.split('(')[1].split(')')[0]
#         words = target.split(')')[1].strip()
#         obj = {'name': name, 'time': time, 'words': words}
#         transcript.append(obj)
#     except:
#         print(target)
In [73]:
transcript = []
prev_time = ''
for t in targets:
    target = t.get_text()
    name = target.split(':')[0]
    
    try: 
        time = target.split('(')[1].split(')')[0]
    except: 
        time = prev_time
    
    try:
        words = target.split('\n')[1].strip()
    
    except:
        print(target)
    
    obj = {'name': name, 'time': time, 'words': words}
    prev_time = time
    transcript.append(obj)
 
In [74]:
df = pd.DataFrame(transcript)
df.to_csv('NLP_debates2020.csv', index=False)

ANALYZE

In [96]:
df = pd.read_csv('NLP_debates2020.csv')
# df

HOW MUCH TIME WAS EACH PERSON SPEAKING?

In [97]:
# PROBLEM: Can't subtract strings
# SOLUTION: Simply turn it into a datetime
# PROBLEM: 01:20 turns into 1 hour twenty minutes instead of 1 minute 20 sceconds
# SOLUTION: Add "00:" to the front of those and turn it into a time object!
# PROBLEM: You can't subtract time objects
# SOLUTION: Turn it into a datetime object
# PROBLEM: You can't convert this to a datetime object
# SOLUTION: Convert it to a "timedelta" object, wtf that is
# PROBLEM: ... it needs to be a string (back where we started?!)
# SOLUTION: df['datetime'] = pd.to_timedelta(df['timestamp'].astype(str))
# ...and NOW we can use .dif()
# PROBLEM: Now that we have the difference, we STILL can't sum it
# SOLUTION: Use dt.total_seconds() (I seriously must be missing something)


from datetime import time

def better_time(t):
    if len(t.split(':')) > 2:
        return t
    else:
        return "00:" + t
    
df['better_time'] = df.apply(lambda x: better_time(x['time']), axis=1)


def convert_time(t):
    try:
        return time.fromisoformat(t)
    except:
        return 'error'
df['timestamp'] = df.apply(lambda x: convert_time(x['better_time']), axis=1)



df['datetime'] = pd.to_timedelta(df['timestamp'].astype(str))
df['time_diff'] = df['datetime'].diff()
df['seconds'] = df['time_diff'].dt.total_seconds()
df
Out[97]:
name time words better_time timestamp datetime time_diff seconds
0 Chris Wallace 01:20 Good evening from the Health Education Campus ... 00:01:20 00:01:20 00:01:20 NaT NaN
1 Chris Wallace 02:10 This debate is being conducted under health an... 00:02:10 00:02:10 00:02:10 00:00:50 50.0
2 Vice President Joe Biden 02:49 How you doing, man? 00:02:49 00:02:49 00:02:49 00:00:39 39.0
3 President Donald J. Trump 02:51 How are you doing? 00:02:51 00:02:51 00:02:51 00:00:02 2.0
4 Vice President Joe Biden 02:51 I’m well. 00:02:51 00:02:51 00:02:51 00:00:00 0.0
... ... ... ... ... ... ... ... ...
785 Chris Wallace 01:10:43 Gentlemen, just say that’s the end of it [cros... 01:10:43 01:10:43 01:10:43 00:00:02 2.0
786 President Donald J. Trump 01:10:47 I want to see an honest ballot count. 01:10:47 01:10:47 01:10:47 00:00:04 4.0
787 Chris Wallace 01:10:48 We’re going to leave it there- 01:10:48 01:10:48 01:10:48 00:00:01 1.0
788 President Donald J. Trump 01:10:49 And I think he does too- 01:10:49 01:10:49 01:10:49 00:00:01 1.0
789 Chris Wallace 01:10:50 … to be continued in more debates as we go on.... 01:10:50 01:10:50 01:10:50 00:00:01 1.0

790 rows × 8 columns

In [99]:
df['number_of_words'] = df.apply(lambda x: len(x['words'].split(' ')), axis=1)
df
Out[99]:
name time words better_time timestamp datetime time_diff seconds number_of_words
0 Chris Wallace 01:20 Good evening from the Health Education Campus ... 00:01:20 00:01:20 00:01:20 NaT NaN 124
1 Chris Wallace 02:10 This debate is being conducted under health an... 00:02:10 00:02:10 00:02:10 00:00:50 50.0 102
2 Vice President Joe Biden 02:49 How you doing, man? 00:02:49 00:02:49 00:02:49 00:00:39 39.0 4
3 President Donald J. Trump 02:51 How are you doing? 00:02:51 00:02:51 00:02:51 00:00:02 2.0 4
4 Vice President Joe Biden 02:51 I’m well. 00:02:51 00:02:51 00:02:51 00:00:00 0.0 2
... ... ... ... ... ... ... ... ... ...
785 Chris Wallace 01:10:43 Gentlemen, just say that’s the end of it [cros... 01:10:43 01:10:43 01:10:43 00:00:02 2.0 17
786 President Donald J. Trump 01:10:47 I want to see an honest ballot count. 01:10:47 01:10:47 01:10:47 00:00:04 4.0 8
787 Chris Wallace 01:10:48 We’re going to leave it there- 01:10:48 01:10:48 01:10:48 00:00:01 1.0 6
788 President Donald J. Trump 01:10:49 And I think he does too- 01:10:49 01:10:49 01:10:49 00:00:01 1.0 6
789 Chris Wallace 01:10:50 … to be continued in more debates as we go on.... 01:10:50 01:10:50 01:10:50 00:00:01 1.0 122

790 rows × 9 columns

In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [100]:
df.to_csv('NLP_debates2020_withTimeDiff.csv', index=False)

OK BUT REALLY, HOW MUCH TIME WAS EACH PERSON SPEAKING

In [101]:
df = pd.read_csv('NLP_debates2020_withTimeDiff.csv')
df
Out[101]:
name time words better_time timestamp datetime time_diff seconds number_of_words
0 Chris Wallace 01:20 Good evening from the Health Education Campus ... 00:01:20 00:01:20 0 days 00:01:20.000000000 NaN NaN 124
1 Chris Wallace 02:10 This debate is being conducted under health an... 00:02:10 00:02:10 0 days 00:02:10.000000000 0 days 00:00:50.000000000 50.0 102
2 Vice President Joe Biden 02:49 How you doing, man? 00:02:49 00:02:49 0 days 00:02:49.000000000 0 days 00:00:39.000000000 39.0 4
3 President Donald J. Trump 02:51 How are you doing? 00:02:51 00:02:51 0 days 00:02:51.000000000 0 days 00:00:02.000000000 2.0 4
4 Vice President Joe Biden 02:51 I’m well. 00:02:51 00:02:51 0 days 00:02:51.000000000 0 days 00:00:00.000000000 0.0 2
... ... ... ... ... ... ... ... ... ...
785 Chris Wallace 01:10:43 Gentlemen, just say that’s the end of it [cros... 01:10:43 01:10:43 0 days 01:10:43.000000000 0 days 00:00:02.000000000 2.0 17
786 President Donald J. Trump 01:10:47 I want to see an honest ballot count. 01:10:47 01:10:47 0 days 01:10:47.000000000 0 days 00:00:04.000000000 4.0 8
787 Chris Wallace 01:10:48 We’re going to leave it there- 01:10:48 01:10:48 0 days 01:10:48.000000000 0 days 00:00:01.000000000 1.0 6
788 President Donald J. Trump 01:10:49 And I think he does too- 01:10:49 01:10:49 0 days 01:10:49.000000000 0 days 00:00:01.000000000 1.0 6
789 Chris Wallace 01:10:50 … to be continued in more debates as we go on.... 01:10:50 01:10:50 0 days 01:10:50.000000000 0 days 00:00:01.000000000 1.0 122

790 rows × 9 columns

In [104]:
dfg = df.groupby('name')['seconds'].sum()
dfg
Out[104]:
name
Chris Wallace                1774.0
President Donald J. Trump     707.0
Vice President Joe Biden     1689.0
                                0.0
Name: seconds, dtype: float64
In [105]:
dfg = df.groupby('name')['number_of_words'].sum()
dfg
Out[105]:
name
Chris Wallace                4674
President Donald J. Trump    7240
Vice President Joe Biden     6618
                                7
Name: number_of_words, dtype: int64
In [122]:
biden = df[df['name'] == 'Vice President Joe Biden']
trump = df[df['name'] == 'President Donald J. Trump']
In [123]:
from collections import Counter
biden_words = biden['words']
trump_words = trump['words']

all_biden_words = ""
for w in biden_words:
    all_biden_words += w

all_trump_words = ""
for w in trump_words:
    all_trump_words += w    
In [124]:
biden_word_count = Counter(all_biden_words.split(' '))
biden_word_count.most_common()
Out[124]:
[('the', 296),
 ('to', 220),
 ('of', 135),
 ('in', 132),
 ('a', 123),
 ('and', 116),
 ('is', 109),
 ('that', 82),
 ('he', 71),
 ('you', 66),
 ('people', 61),
 ('going', 60),
 ('have', 59),
 ('not', 58),
 ('be', 55),
 ('we', 48),
 ('it', 46),
 ('And', 46),
 ('what', 45),
 ('I', 44),
 ('He', 43),
 ('are', 41),
 ('for', 37),
 ('on', 37),
 ('about', 35),
 ('this', 30),
 ('who', 29),
 ('by', 29),
 ('because', 28),
 ('get', 27),
 ('has', 27),
 ('his', 26),
 ('with', 25),
 ('[crosstalk', 25),
 ('was', 25),
 ('they', 23),
 ('out', 23),
 ('fact', 23),
 ('all', 23),
 ('The', 22),
 ('as', 22),
 ('do', 22),
 ('at', 21),
 ('your', 20),
 ('should', 19),
 ('We', 19),
 ('it’s', 19),
 ('He’s', 18),
 ('more', 18),
 ('there', 18),
 ('did', 18),
 ('so', 17),
 ('way', 17),
 ('like', 17),
 ('able', 17),
 ('just', 17),
 ('can', 17),
 ('make', 17),
 ('take', 17),
 ('been', 16),
 ('will', 16),
 ('if', 16),
 ('it.', 16),
 ('They', 16),
 ('how', 16),
 ('You', 16),
 ('sure', 16),
 ('when', 15),
 ('we’re', 15),
 ('he’s', 15),
 ('my', 15),
 ('doesn’t', 15),
 ('tax', 15),
 ('It’s', 14),
 ('want', 14),
 ('know', 14),
 ('don’t', 14),
 ('up', 14),
 ('say', 13),
 ('I’m', 13),
 ('fact,', 13),
 ('got', 13),
 ('no', 13),
 ('being', 13),
 ('American', 12),
 ('that’s', 12),
 ('down', 12),
 ('him', 12),
 ('look', 12),
 ('said', 12),
 ('now', 11),
 ('their', 11),
 ('from', 11),
 ('If', 11),
 ('them', 11),
 ('That’s', 11),
 ('would', 11),
 ('This', 11),
 ('than', 11),
 ('It', 11),
 ('an', 10),
 ('that,', 10),
 ('million', 10),
 ('had', 10),
 ('were', 10),
 ('here’s', 10),
 ('deal.', 10),
 ('way,', 10),
 ('those', 10),
 ('talking', 10),
 ('which', 9),
 ('talk', 9),
 ('never', 9),
 ('didn’t', 9),
 ('vote', 8),
 ('United', 8),
 ('They’re', 8),
 ('made', 8),
 ('very', 8),
 ('one', 8),
 ('many', 8),
 ('done', 8),
 ('Number', 8),
 ('number', 8),
 ('said,', 8),
 ('back', 8),
 ('or', 8),
 ('even', 8),
 ('lot', 8),
 ('but', 8),
 ('these', 8),
 ('open', 8),
 ('they’re', 8),
 ('election', 7),
 ('wants', 7),
 ('went', 7),
 ('could', 7),
 ('man', 7),
 ('deal', 7),
 ('plan', 7),
 ('go', 7),
 ('it,', 7),
 ('jobs', 7),
 ('create', 7),
 ('you’re', 7),
 ('President', 6),
 ('thousands', 6),
 ('only', 6),
 ('that.', 6),
 ('trying', 6),
 ('having', 6),
 ('But', 6),
 ('same', 6),
 ('until', 6),
 ('What', 6),
 ('Well,', 6),
 ('shut', 6),
 ('ballot', 6),
 ('one.', 6),
 ('everything', 6),
 ('plan.', 6),
 ('vote.', 6),
 ('us', 6),
 ('bring', 6),
 ('economy', 6),
 ('them,', 6),
 ('own', 6),
 ('end', 6),
 ('you.', 6),
 ('His', 6),
 ('totally', 6),
 ('president', 6),
 ('can’t', 6),
 ('ballots', 6),
 ('right', 5),
 ('here', 5),
 ('Affordable', 5),
 ('Care', 5),
 ('before', 5),
 ('money', 5),
 ('then', 5),
 ('simply', 5),
 ('does', 5),
 ('me', 5),
 ('me,', 5),
 ('still', 5),
 ('wrong', 5),
 ('any', 5),
 ('two,', 5),
 ('won’t', 5),
 ('about.', 5),
 ('let', 5),
 ('When', 5),
 ('why', 5),
 ('knew', 5),
 ('give', 5),
 ('job', 5),
 ('need', 5),
 ('someone', 5),
 ('where', 5),
 ('says', 5),
 ('terms', 5),
 ('care', 5),
 ('asked', 5),
 ('work', 5),
 ('put', 5),
 ('COVID', 5),
 ('guy', 5),
 ('administration', 5),
 ('our', 5),
 ('things', 5),
 ('There’s', 5),
 ('police', 5),
 ('out.', 5),
 ('violence', 5),
 ('is,', 5),
 ('support', 5),
 ('millions', 5),
 ('doing', 4),
 ('States', 4),
 ('States.', 4),
 ('already', 4),
 ('see', 4),
 ('what’s', 4),
 ('now,', 4),
 ('into', 4),
 ('she', 4),
 ('other', 4),
 ('pre-existing', 4),
 ('there’s', 4),
 ('away', 4),
 ('knows', 4),
 ('big', 4),
 ('saying', 4),
 ('Biden', 4),
 ('under', 4),
 ('am', 4),
 ('COVID.', 4),
 ('well', 4),
 ('him.', 4),
 ('matter', 4),
 ('whole', 4),
 ('you,', 4),
 ('tell', 4),
 ('help', 4),
 ('ever', 4),
 ('hasn’t', 4),
 ('talks', 4),
 ('position', 4),
 ('voting', 4),
 ('day', 4),
 ('In', 4),
 ('between', 4),
 ('serious', 4),
 ('determine', 4),
 ('great', 4),
 ('down.', 4),
 ('another', 4),
 ('trust', 4),
 ('do,', 4),
 ('No', 4),
 ('time', 4),
 ('idea', 4),
 ('Why', 4),
 ('making', 4),
 ('less', 4),
 ('economic', 4),
 ('every', 4),
 ('blew', 4),
 ('I’ll', 4),
 ('good', 4),
 ('happened', 4),
 ('stay', 4),
 ('problem.', 4),
 ('build', 4),
 ('Green', 4),
 ('New', 4),
 ('mail-in', 4),
 ('How', 3),
 ('first', 3),
 ('forward', 3),
 ('Mr.', 3),
 ('middle', 3),
 ('voted', 3),
 ('thing', 3),
 ('outcome', 3),
 ('rid', 3),
 ('insurance', 3),
 ('justice,', 3),
 ('opposed', 3),
 ('court,', 3),
 ('down,', 3),
 ('pay', 3),
 ('passed', 3),
 ('election.', 3),
 ('next', 3),
 ('wipe', 3),
 ('Democratic', 3),
 ('200,000', 3),
 ('died', 3),
 ('happy', 3),
 ('point', 3),
 ('now.', 3),
 ('public', 3),
 ('call', 3),
 ('costs', 3),
 ('healthcare', 3),
 ('recession.', 3),
 ('since', 3),
 ('that’ll', 3),
 ('wasn’t', 3),
 ('is.', 3),
 ('laid', 3),
 ('do?', 3),
 ('people.', 3),
 ('told', 3),
 ('doing.', 3),
 ('businesses', 3),
 ('money.', 3),
 ('together', 3),
 ('save', 3),
 ('folks', 3),
 ('Director', 3),
 ('gone', 3),
 ('gone.', 3),
 ('maybe', 3),
 ('some', 3),
 ('year', 3),
 ('out,', 3),
 ('no,', 3),
 ('no.', 3),
 ('try', 3),
 ('One', 3),
 ('Because', 3),
 ('A', 3),
 ('provide', 3),
 ('mean,', 3),
 ('head', 3),
 ('everybody', 3),
 ('billion', 3),
 ('president.', 3),
 ('People', 3),
 ('states', 3),
 ('America', 3),
 ('significant', 3),
 ('fix', 3),
 ('anything', 3),
 ('aren’t', 3),
 ('Not', 3),
 ('nothing', 3),
 ('makes', 3),
 ('eliminate', 3),
 ('four', 3),
 ('billions', 3),
 ('trade', 3),
 ('way.', 3),
 ('before.', 3),
 ('discredited.', 3),
 ('son', 3),
 ('true.', 3),
 ('America.', 3),
 ('we’ve', 3),
 ('gas', 3),
 ('held', 3),
 ('happen.', 3),
 ('different', 3),
 ('together.', 3),
 ('15%', 3),
 ('I’ve', 3),
 ('FBI', 3),
 ('president,', 3),
 ('much', 3),
 ('That', 3),
 ('oil', 3),
 ('We’re', 3),
 ('jobs.', 3),
 ('jobs,', 3),
 ('Paris', 3),
 ('paying', 3),
 ('vote,', 3),
 ('evidence', 3),
 ('votes', 3),
 ('counted,', 3),
 ('accepted.', 3),
 ('ballots,', 3),
 ('this,', 2),
 ('Supreme', 2),
 ('Court', 2),
 ('Senators', 2),
 ('happen', 2),
 ('wait', 2),
 ('elect', 2),
 ('stake', 2),
 ('running', 2),
 ('Act,', 2),
 ('20', 2),
 ('health', 2),
 ('goes', 2),
 ('fine', 2),
 ('person.', 2),
 ('rights', 2),
 ('woman', 2),
 ('charge', 2),
 ('they’ll', 2),
 ('well.', 2),
 ('Those', 2),
 ('companies', 2),
 ('forward.', 2),
 ('one,', 2),
 ('Obamacare', 2),
 ('increase', 2),
 ('do.', 2),
 ('me.', 2),
 ('Party', 2),
 ('approved', 2),
 ('of.', 2),
 ('seven', 2),
 ('also', 2),
 ('healthcare.He', 2),
 ('not.', 2),
 ('poor', 2),
 ('Medicaid', 2),
 ('most', 2),
 ('option.', 2),
 ('vast', 2),
 ('majority', 2),
 ('beat', 2),
 ('tonight', 2),
 ('far', 2),
 ('through', 2),
 ('away.', 2),
 ('that.He', 2),
 ('sends', 2),
 ('power.', 2),
 ('drug', 2),
 ('almost', 2),
 ('become', 2),
 ('issue', 2),
 ('Vote', 2),
 ('know,', 2),
 ('answer', 2),
 ('really', 2),
 ('As', 2),
 ('over', 2),
 ('world’s', 2),
 ('addition', 2),
 ('1000', 2),
 ('dying.', 2),
 ('anything.', 2),
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 ('China', 2),
 ('themselves', 2),
 ('dangerous', 2),
 ('ask', 2),
 ('Xi', 2),
 ('nothing.', 2),
 ('waited', 2),
 ('providing', 2),
 ('order', 2),
 ('keep', 2),
 ('open.', 2),
 ('schools', 2),
 ('cost', 2),
 ('golf', 2),
 ('Democrats', 2),
 ('Republicans', 2),
 ('needs', 2),
 ('reason', 2),
 ('look,', 2),
 ('home.', 2),
 ('COVID?', 2),
 ('lost', 2),
 ('couldn’t', 2),
 ('CDC', 2),
 ('mask,', 2),
 ('half', 2),
 ('Just', 2),
 ('mask.', 2),
 ('vaccine,', 2),
 ('all.', 2),
 ('By', 2),
 ('vaccine.', 2),
 ('vaccine', 2),
 ('year,', 2),
 ('God', 2),
 ('question', 2),
 ('pointed', 2),
 ('people…', 2),
 ('looked', 2),
 ('market.', 2),
 ('unless', 2),
 ('I’d', 2),
 ('ability', 2),
 ('need.', 2),
 ('meet', 2),
 ('them.', 2),
 ('sits', 2),
 ('think', 2),
 ('respond', 2),
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 ('difference.', 2),
 ('wore', 2),
 ('social', 2),
 ('person', 2),
 ('last', 2),
 ('came', 2),
 ('worried', 2),
 ('effect.', 2),
 ('Come', 2),
 ('masks,', 2),
 ('difference', 2),
 ('billionaires', 2),
 ('proposal,', 2),
 ('home,', 2),
 ('small', 2),
 ('towns', 2),
 ('America,', 2),
 ('worked', 2),
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 ('school', 2),
 ('national', 2),
 ('emergency.', 2),
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 ('Look,', 2),
 ('advantage', 2),
 ('code.', 2),
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 ('federal', 2),
 ('buildings', 2),
 ('penny', 2),
 ('single', 2),
 ('inherited', 2),
 ('recovery', 2),
 ('booming', 2),
 ('brought', 2),
 ('done.', 2),
 ('art', 2),
 ('true.None', 2),
 ('everyone,', 2),
 ('testified', 2),
 ('fired', 2),
 ('family,', 2),
 ('people,', 2),
 ('absolutely', 2),
 ('true.That’s', 2),
 ('equity', 2),
 ('walked', 2),
 ('true,', 2),
 ('remember', 2),
 ('coming', 2),
 ('killed', 2),
 ('Floyd', 2),
 ('killed,', 2),
 ('peaceful', 2),
 ('protest', 2),
 ('White', 2),
 ('use', 2),
 ('church', 2),
 ('hold', 2),
 ('after', 2),
 ('dog', 2),
 ('African', 2),
 ('something', 2),
 ('500', 2),
 ('Americans.', 2),
 ('did.', 2),
 ('law', 2),
 ('enforcement', 2),
 ('officers', 2),
 ('bad', 2),
 ('accountable.', 2),
 ('entire', 2),
 ('These', 2),
 ('cops', 2),
 ('grow', 2),
 ('bit', 2),
 ('country', 2),
 ('cannot', 2),
 ('racism', 2),
 ('crime', 2),
 ('suburbs.', 2),
 ('white', 2),
 ('suburbs', 2),
 ('failure', 2),
 ('defunding', 2),
 ('police,', 2),
 ('local', 2),
 ('calls', 2),
 ('$400', 2),
 ('assistance.', 2),
 ('up.', 2),
 ('former', 2),
 ('vice', 2),
 ('clear', 2),
 ('keeps', 2),
 ('fire', 2),
 ('weaker,', 2),
 ('divided', 2),
 ('violent.', 2),
 ('left', 2),
 ('regard', 2),
 ('Putin', 2),
 ('son,', 2),
 ('behind', 2),
 ('proud', 2),
 ('report', 2),
 ('standards', 2),
 ('renewable', 2),
 ('energy', 2),
 ('coal', 2),
 ('plant', 2),
 ('move', 2),
 ('fleet', 2),
 ('emit', 2),
 ('heat', 2),
 ('creating', 2),
 ('carbon', 2),
 ('$20', 2),
 ('Look', 2),
 ('hurricanes,', 2),
 ('hard,', 2),
 ('come', 2),
 ('Deal', 2),
 ('director,', 2),
 ('polls', 2),
 ('counted.', 2),
 ('sent', 2),
 ('day.', 2),
 ('count', 2),
 ('for,', 2),
 ('somehow', 2),
 ('doing,', 1),
 ('man?I’m', 1),
 ('well.Well,', 1),
 ('all,', 1),
 ('thank', 1),
 ('looking', 1),
 ('President.The', 1),
 ('nominee', 1),
 ('occurs', 1),
 ('chance', 1),
 ('already.', 1),
 ('started.', 1),
 ('Tens', 1),
 ('wait.', 1),
 ('express', 1),
 ('view', 1),
 ('Vice', 1),
 ('President.Now,', 1),
 ('President’s', 1),
 ('clear,', 1),
 ('Act.', 1),
 ('ran', 1),
 ('governing', 1),
 ('strip', 1),
 ('court.', 1),
 ('seems', 1),
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In [125]:
trump_word_count = Counter(all_trump_words.split(' '))
trump_word_count.most_common()
Out[125]:
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 ('you', 172),
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 ('V.', 1),
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 ...]
In [130]:
set_biden = set(all_biden_words.split(' '))
set_trump = set(all_trump_words.split(' '))
In [131]:
print(len(set_biden))
print(len(set_trump))
1827
1890
In [133]:
set_biden
Out[133]:
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 'testify,',
 'market.',
 'penny',
 'warming.',
 'healthcare.Yes.It',
 'students',
 'prevailing',
 'afraid',
 '10',
 'Accord.',
 'talk',
 'for',
 'panic',
 'constitution.',
 'Chrysler',
 'started.',
 'prosecuted',
 'Conway.She',
 'year.',
 'having',
 'racists',
 'bench,',
 'also',
 'lucky',
 'Keep',
 'acknowledged',
 'No',
 'went',
 '91',
 'psychologist',
 'taken',
 'help.Because',
 'speaking',
 'good-paying',
 'millions',
 'trust',
 '00:08:48].No',
 'That',
 'that.I',
 'civil',
 'Medicaid.',
 'many',
 'happen,',
 'doesn’t',
 'lost',
 'happening,',
 'Bernie',
 'deadly',
 'serious',
 'view',
 '“No,',
 'president',
 'spends',
 'discredited.That',
 'torn',
 'respond',
 'here.',
 'power.',
 'waited.',
 'our',
 'person.',
 'the-',
 'what?',
 'segment,',
 'no,',
 'gasoline',
 'ripped',
 'wrong,',
 'states.',
 'you’ve',
 'apples.',
 'debates',
 'here',
 'schools',
 'Michigan.',
 'clear.',
 'eyes,',
 'group',
 'votes,',
 'doing.',
 'two',
 '20,',
 'years.',
 'disease.',
 'healthcare.He',
 '00:24:25]This',
 'taxes.',
 'declared',
 'hole-…',
 '4%',
 '01:05:32]…',
 'it?',
 'go',
 'mom',
 'trouble.Look,',
 'This',
 'media,',
 'bad',
 'foreign',
 'tune',
 '00:16:04]?Yeah,',
 'threat',
 'never',
 'fine.',
 '“There',
 'mileage',
 'working',
 'only',
 'military',
 'up.You’re',
 'same',
 'said.Every',
 'simply',
 'bleach',
 'Governors',
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 'true.That’s',
 'insults',
 'puppy.',
 'right',
 'social',
 'still',
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 'true.None',
 'renewable',
 '00:59:17]',
 'why',
 'said-Yes,',
 'down',
 'contracted',
 'wishful',
 'were',
 'lucky.',
 'thirdly,',
 'Methane.',
 'counties',
 'person,',
 'died',
 'killed.',
 'feelings,',
 'know,',
 'nothing.',
 '$400',
 'state,',
 'who,',
 'billions',
 'who',
 'Figure',
 'contracting',
 'of,',
 'those',
 'businesses.',
 'Claymont',
 'enforcement',
 'that.He',
 'legitimate.Mail',
 'new',
 'reporter',
 'crisis.',
 'better',
 'saying,',
 '01:07:24]-Yes.',
 'pay',
 'are.',
 'accountable',
 'all,',
 'question,',
 'Excuse',
 'Wuhan',
 'budget',
 'Scranton.',
 'me?No,',
 'weather,',
 'whatever',
 'Nor',
 'regard',
 'wants',
 'entire',
 'means',
 'Act',
 'falling',
 'day.',
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 'liar.God,',
 'countries',
 'feel.Vote',
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 'two.Look,',
 'said,',
 'wanted',
 'companies',
 'blew',
 'hundred',
 'education',
 'your',
 'sense',
 'of.',
 '00:04:44].Yeah.The',
 'may-Yeah,',
 'bile',
 'qualifies',
 'lives.',
 '$1',
 'Stop',
 'two.',
 'man.They',
 'government',
 'one.',
 'run',
 'conditions',
 'why?',
 'opened',
 'Well,',
 'took',
 'automobiles',
 'Party.I',
 'you’d',
 'election.That’s',
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 'depression',
 'Wall',
 'rally.Look,',
 'idea',
 'African',
 'answer,',
 'Senators',
 'their',
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 'Just',
 'energy.',
 'like,',
 'Many',
 'All',
 'coal',
 'do.',
 'iwillvote.com,',
 'standards',
 '21%.',
 'plan.',
 'another',
 'America.',
 'under',
 'votes-I',
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 'majority',
 '00:24:32]',
 'disastrous',
 '“It',
 'between',
 'help',
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 'proposal.Yeah,',
 'court.Donald',
 'true.It',
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 'screwing',
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 '200',
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 'Vote,',
 'fool',
 'we,',
 'calm',
 'scientist.',
 '500',
 'worked',
 'way.And',
 'okay.”',
 'true.Look,',
 'appropriate,',
 'wins',
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 'knows',
 'African-American',
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 'finish.Not',
 'world,',
 'die',
 'whistle,',
 'Biden',
 'faith.',
 'got',
 'us,',
 'trouble',
 'employers',
 'masks',
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 'in',
 'right.Good',
 'day',
 'fire',
 'election.',
 'incentives',
 'Mr.',
 'V.',
 'would',
 'bunker',
 'needing',
 'relationship',
 'turn.I',
 'option.',
 'teachers',
 'trying',
 'Good',
 'woman',
 'son,',
 'save',
 'emit',
 'evidence',
 'federal,',
 'chiefs,',
 'His',
 'Roe',
 'classic-Will',
 'Scranton',
 'meet',
 'rantings',
 'effect.',
 'additional',
 'Come',
 '00:24:05]That',
 'Care',
 'putting',
 'rallies',
 'carrying',
 'Miraculous,',
 'Billionaires',
 'two.Number',
 'America,',
 'More',
 'numbers.',
 'radical',
 'debate-Because',
 'say,',
 'it’d',
 'smarter,',
 'Floyd.',
 'law.',
 'them',
 '00:55:51]-No.That',
 'able',
 'support',
 'People',
 'offered',
 'across',
 'Suburbs',
 'proud',
 'It’s',
 'school',
 'young',
 'governing',
 'Nothing.',
 'protective',
 'Service',
 'basically',
 'peaceful',
 'give',
 'steel,',
 'vaccine',
 'light',
 'situation',
 'asked',
 'resent-I’m',
 'friends',
 'excuse',
 'running',
 'Florida,',
 'polluting',
 '23',
 'dollars.Because',
 'Senate.',
 'jobs.',
 'protest',
 'way-Do',
 'court',
 'else',
 'cuts.And',
 'somehow',
 'Stand',
 'apart.',
 'coming',
 'none,',
 'dog',
 'Nobody’s',
 'recovery',
 'for,',
 '“I’m',
 'pressure',
 'stay',
 'socially',
 'rid',
 'Democrats',
 'all?',
 'Director',
 'qualify',
 'said',
 'years,',
 'number,',
 'Pray',
 'poll',
 'CDC',
 'mask',
 'matter',
 'manifesto,',
 'irresponsible',
 'administration.',
 'this',
 'America',
 'true.',
 'different',
 'mean',
 'grow',
 'safe.People',
 'clown.',
 'Sanders.I',
 'racist',
 'know',
 'way,',
 'this.',
 'heroes.And',
 'PPE,',
 'tell',
 'morning',
 '15%',
 'her',
 'sicker,',
 'come',
 'eye',
 'infected',
 'probably',
 'And,',
 'not-Not',
 'wasn’t',
 'methane',
 'we’ve',
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 'was',
 'past,',
 'seems',
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 'elected',
 'infrastructure',
 'mask.',
 'President-',
 '750',
 'old',
 'because,',
 'men',
 'significantly',
 'violent',
 'nominee',
 'decency.',
 'anything.I',
 'Fewer',
 'hour,',
 'exact',
 'is.',
 'plan,',
 'large',
 'poor',
 'body',
 'family’s',
 'plan',
 'actually',
 'Americans',
 'totally',
 'military…',
 'Accord.For',
 'night.',
 'fund',
 'single',
 'soldiers.By',
 'Senators.I’m',
 'facing',
 'it.Yeah,',
 'still.',
 'defunding',
 'matters.No',
 'analysis',
 'table',
 'bring',
 'create',
 'away',
 'healthcare',
 'cars',
 'people.Look-…',
 'Deal.Prepare',
 'being',
 'watch.He',
 'themselves',
 'you',
 '500,000',
 'floods,',
 'as',
 '200,000',
 'phone',
 'code',
 'happen',
 'saying',
 'all',
 'fleet',
 'stations',
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 'true.Is',
 'Star.',
 'require',
 'truth.',
 'absolutely',
 'Green',
 'back',
 'literally,',
 'chair',
 '00:58:14]',
 'approved',
 'gratitude',
 'done.14,000',
 'world’s',
 'point,',
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 'real',
 'Irish',
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 'nation,',
 'fundamentally',
 'things',
 'oil',
 'administration',
 'ask',
 'That’s',
 'person',
 'hole.',
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 'couldn’t',
 'healthcare.Because',
 'including',
 'votes',
 'quickly,',
 'occurs',
 'done.',
 'don’t,',
 'more',
 'no',
 'constantly',
 'highways',
 'COVID?',
 'first',
 'gigantic',
 'torches,',
 'demeaning',
 'miracle.',
 'rising',
 'which',
 'Manufacturing',
 'point.It’s',
 'risk',
 '1000',
 'population,',
 'established',
 'open',
 'suburbs.',
 'already-And',
 'opposed',
 'ships,',
 'some',
 'watchers',
 'weapon',
 'trap',
 'fact',
 'last',
 'discredited.My',
 'not…',
 'sufficient',
 'mistake',
 'just',
 'office,',
 'counting',
 'forward.',
 'July,',
 'policy.',
 'Floyd',
 'cuts.',
 'dying',
 'stock',
 'not.',
 'deaths',
 'true.And',
 'about.I',
 'concerned',
 'anyone',
 'Peaceful',
 'spend',
 'changed',
 'Schumer,',
 'that’ll',
 '17%,',
 'suburb',
 'women.',
 'warm',
 'perfected',
 'at',
 'racial',
 'safer.',
 'one,',
 'Hispanic',
 'appropriate.',
 'guy,',
 'Act?I’m',
 'lives,',
 'they',
 'line.',
 'inherited',
 'Those',
 'United',
 'allies.By',
 'want',
 'ridiculous.Absolutely',
 'overtaking',
 'proposal.That',
 '1950.',
 'Some',
 'dangerous',
 'police',
 'issue.',
 'oil.',
 'tested?',
 'I’d',
 'true.Not',
 'related',
 'me.',
 'You,',
 'what’s',
 'talking',
 'written',
 'spokesperson',
 'laid',
 'will…',
 'final',
 'justice,',
 'pieces',
 'distribution',
 'wouldn’t',
 'problems',
 'And',
 '$300',
 'Once',
 'buddy.All',
 'distanced',
 'relaxed',
 'much',
 'loser.',
 'American',
 'end',
 'billion',
 'came',
 'she’s',
 'enforced.',
 'determine',
 'time',
 'remember',
 'Breonna',
 'Barisma-He',
 'rest',
 'anything.',
 'use',
 'outcome.',
 '00:17:14].Sure.Whatever',
 'bit',
 'costs',
 'emergency.',
 'manufacturing',
 'this.The',
 'correct',
 'thrown',
 'standing',
 'color.',
 '00:15:43].The',
 'not.It',
 'protest.Nobody’s',
 'caused',
 'person.He',
 'Instead',
 'true.My',
 'fields,',
 'going',
 'Klan.',
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 'now,',
 'safely.',
 'raised',
 'lot',
 'settle',
 'died,',
 'ballot',
 'home.',
 '$750',
 'hurricanes,',
 'done',
 'can-by',
 'respond.',
 'crisis',
 'corporate',
 'living',
 'Tens',
 'proposed',
 'vehicles.',
 'record',
 '00:48:23]',
 'when…',
 'office.',
 'officers,',
 'Accord',
 'anybody.',
 'States.',
 'instead',
 'through',
 'family.',
 'speak.',
 'build',
 'up,',
 'that-Play',
 'three.No.This',
 'minute?Nancy',
 'fact.It’s',
 'economy,',
 'count',
 'Republican,',
 'absorbed',
 'Affordable',
 'automatically',
 'me,',
 'then',
 'scientists.Everybody',
 'driving',
 'this.”',
 'over.',
 'act,',
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 'a-',
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 'crime-I’m',
 'be…That',
 'my',
 'disagrees',
 'very',
 'groups,',
 'anymore.',
 'believe',
 'mask,',
 'lines.',
 'maybe',
 'guy-',
 'early',
 'eliminate',
 'Bible.',
 'on',
 'Catholics,',
 'opportunity',
 'does,',
 'up.Let',
 'own',
 'Now,',
 'during',
 'divided.',
 'itself',
 'carbon',
 'occur',
 'generate',
 'without',
 'warning',
 'open?',
 'Act,',
 'She',
 'affidavit.',
 'sure',
 'private',
 'acknowledging',
 'worst',
 'show',
 'such',
 'stake',
 'Bishop',
 'possible.',
 'Medal.',
 'election',
 'down,',
 'you-…',
 'insisting',
 'Beau',
 'Desk',
 'savior',
 'counted,',
 'plan.I',
 'Hunter?That’s',
 'listen',
 'aware',
 'man?This',
 'even',
 'decide',
 'walk',
 'guess',
 'break.Well,',
 'see',
 'all-It’s',
 'happen.',
 'pre-existing',
 'and/or',
 'Look,',
 'sorted',
 'hatred,',
 'since',
 'heat',
 'worried',
 'You',
 'insensitivity.',
 'ridiculous.Violent',
 'distance.',
 'steel.',
 'What',
 'elect',
 'discussion.Number',
 'does',
 'people,',
 'is.”',
 'drug',
 'encouraged',
 'honest.',
 'integrated.',
 'man?I’m',
 'economic',
 'How',
 'He',
 'director,',
 'divided',
 'because',
 'pregnancy.',
 'growth,',
 'Close',
 'former',
 'should',
 'safe.Show',
 'one.Number',
 'costing',
 'forest.',
 'yapping,',
 'order',
 'one’s',
 'change,',
 'art',
 '“Here’s',
 'wore',
 'spent',
 'wait.',
 'pray',
 'veins',
 'race',
 'First',
 'I.Why',
 'rile',
 'plants-Pardon',
 'thought.',
 'he',
 'Biden,',
 'way',
 'state',
 'black',
 'country',
 'Court',
 'left',
 'statements',
 'Vice-That',
 'community.I’ve',
 'expand',
 'walked',
 'it',
 'wherewithal',
 'reason.So',
 'environment',
 'little',
 'give,',
 'Oval',
 'pointed',
 'clear',
 'hurricanes.',
 'telling',
 'did,',
 'cops',
 'that.',
 'sometime',
 'discredited.',
 'down.I',
 'War,',
 'enrolled',
 'zero,',
 'written,',
 '15',
 'that.Just',
 'thoroughly',
 'dotting',
 'pays',
 'don’t',
 'Democrats.',
 'company',
 'States',
 'In',
 'returns.Show',
 'problem.',
 'It',
 'country,',
 'oath',
 'totally-Totally',
 'today.',
 'matter,',
 'lies.',
 'fine',
 'economy-Let',
 'family,',
 'weaker,',
 'businesses',
 'equality',
 'organization-…',
 'ran',
 'hard',
 'outcome',
 'nuclear',
 'plant',
 'Patriot',
 'company-By',
 'winner',
 'Homeland',
 'well',
 '28%.',
 'nomination',
 'look',
 'back?',
 'recession.',
 'me',
 'police,',
 'Deal',
 'two,',
 'poor,',
 'China',
 'shush',
 'point',
 'House',
 'where',
 'Trump',
 'charge',
 'offices.',
 'money,',
 'well.',
 'four',
 'weatherized',
 'this-',
 'us',
 'exist',
 'was.',
 'done-The',
 'the-Yes.',
 'time.There',
 'Security',
 'strike',
 '…',
 'profligate',
 'voting',
 'can',
 'smart',
 'delivers',
 'folks',
 'jobs',
 'trillion',
 'will',
 'assistance.',
 '00:23:57]Well,',
 'church',
 'January,',
 'storms',
 'seventh,',
 'scientist.God.This',
 'need.',
 'quiet',
 'up?',
 'They',
 'by',
 'job',
 'Putin’s',
 'every',
 'whole',
 'division.This',
 'fact,',
 'held',
 'gathering',
 'March,',
 'dollars.',
 ...}
In [137]:
 
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-137-4334b5c4db64> in <module>
----> 1 targets.get_text()

~/.pyenv/versions/3.7.3/lib/python3.7/site-packages/bs4/element.py in __getattr__(self, key)
   2164         """Raise a helpful exception to explain a common code fix."""
   2165         raise AttributeError(
-> 2166             "ResultSet object has no attribute '%s'. You're probably treating a list of elements like a single element. Did you call find_all() when you meant to call find()?" % key
   2167         )

AttributeError: ResultSet object has no attribute 'get_text'. You're probably treating a list of elements like a single element. Did you call find_all() when you meant to call find()?
In [143]:
for t in targets:
    print(t.find('a').get_text())
01:20
02:10
02:49
02:51
02:51
03:11
04:01
04:53
05:22
05:29
05:34
05:36
06:12
07:08
07:34
08:01
08:02
08:24
08:25
08:29
08:31
08:32
08:33
08:58
09:00
09:02
09:05
09:08
09:09
09:11
09:12
09:39
09:47
09:48
09:58
10:00
10:04
10:04
10:05
10:06
10:11
10:25
10:31
10:32
10:33
10:34
10:35
10:36
10:40
10:51
10:51
11:05
11:05
11:07
11:08
11:11
11:11
11:25
11:26
11:28
11:31
11:32
11:35
11:36
11:38
11:38
11:44
11:44
11:46
11:52
11:53
12:16
12:40
12:40
12:41
12:42
13:03
13:04
13:06
13:17
13:17
13:23
13:25
13:26
13:29
13:32
13:57
14:04
14:05
14:06
14:10
14:10
14:12
14:12
14:14
14:17
14:18
14:21
14:23
14:23
14:25
14:25
14:34
14:41
14:43
14:44
14:46
14:47
14:51
14:55
14:57
14:58
14:58
14:59
15:01
15:13
15:14
15:20
15:21
15:41
15:45
15:46
15:47
15:49
15:54
15:59
16:42
16:45
16:45
16:47
16:48
16:50
16:52
16:52
17:13
17:15
17:16
17:55
18:05
18:07
18:08
18:09
18:12
18:17
18:18
18:18
18:20
18:23
18:25
18:25
18:27
18:30
18:35
18:37
18:37
18:40
19:33
20:30
20:43
20:43
20:57
20:57
20:57
20:57
21:30
21:31
21:31
22:06
22:07
22:54
23:15
23:18
23:25
23:27
23:32
23:33
23:57
24:03
24:25
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-143-f33b47045dac> in <module>
      1 for t in targets:
----> 2     print(t.find('a').get_text())

AttributeError: 'NoneType' object has no attribute 'get_text'
In [ ]:
[]