Import Library¶
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import tweepy
from textblob import TextBlob
from wordcloud import WordCloud
from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
import pandas as pd
import numpy as np
import re
import matplotlib.pyplot as plt
import config
plt.style.use('fivethirtyeight')
Twitter Authentication¶
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# Create the authentication object
authenticate = tweepy.OAuthHandler(config.CONSUMER_KEY, config.CONSUMER_SECRET)
# Set the access token and access token secret
authenticate.set_access_token(config.ACCESS_TOKEN, config.ACCESS_TOKEN_SECRET)
# Create the API object while passing in the auth information
api = tweepy.API(authenticate, wait_on_rate_limit = True)
Extract Tweets from the Twitter user¶
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posts = api.user_timeline(screen_name = "realDonaldTrump", count=100, lang= "en", tweet_mode="extended")
# Print the last 5 tweet from the account
print("Show the 5 recent tweets: \n")
i = 1
for tweet in posts[0:5]:
print(str(i) + ') ' + tweet.full_text + '\n')
i = i + 1
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# Create a dataframe with a column called Tweets
df = pd.DataFrame([tweet.full_text for tweet in posts], columns=['Tweets'])
df.head()
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# Create a function to clean the tweets
def cleanText(text):
text = re.sub(r'@[A-Za-z0-9]+', '', text) # Removed @mentions
text = re.sub(r'#', '', text) #Removing the '#' symbol
text = re.sub(r'RT[\s]+', '', text) # Removing RT
text = re.sub(r'https?:\/\/\S+', '', text) # Remove the hyper link
return text
df['Tweets'] = df['Tweets'].apply(cleanText)
df
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# Create a function to get the subjectivity
def getSubjectivity(text):
return TextBlob(text).sentiment.subjectivity
# Create a function to get the polarity
def getPolarity(text):
return TextBlob(text).sentiment.polarity
df['Subjectivity'] = df['Tweets'].apply(getSubjectivity)
df['Polarity'] = df['Tweets'].apply(getPolarity)
df
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Word Cloud¶
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allWords = ' '.join([twts for twts in df['Tweets']])
wordCloud = WordCloud(width = 500, height=300, random_state = 21, max_font_size = 119).generate(allWords)
plt.imshow(wordCloud, interpolation = 'bilinear')
plt.axis('off')
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# Create a function to compute the negative, neutral, and positive analysis
def getAnalysis(score):
if score < 0:
return 'Negative'
elif score == 0:
return 'Neutral'
else:
return 'Positive'
df['Analysis'] = df['Polarity'].apply(getAnalysis)
df
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Print all of the positive tweets¶
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j = 1
sortedDF = df.sort_values(by=['Polarity'])
for i in range(0, sortedDF.shape[0]):
if(sortedDF['Analysis'][i] == 'Positive'):
print(str(j) + ') ' + sortedDF['Tweets'][i])
print()
j = j + 1
Print the negative tweets¶
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j = 1
sortedDF = df.sort_values(by=['Polarity'], ascending='False')
for i in range(0, sortedDF.shape[0]):
if(sortedDF['Analysis'][i] == 'Negative'):
print(str(j) + ')' + sortedDF['Tweets'][i])
print()
j = j + 1
Plot the polarity and the subjectivity¶
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plt.figure(figsize=(8,6))
for i in range(0, df.shape[0]):
plt.scatter(df['Polarity'][i], df['Subjectivity'][i], color='Blue')
plt.title('Sentiment Analysis')
plt.xlabel('Polarity')
plt.ylabel('Subjectivity')
plt.show()
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# Get the percentage of positive tweets
ptweets = df[df.Analysis == 'Positive']
ptweets = ptweets['Tweets']
round((ptweets.shape[0] / df.shape[0]) * 100, 1)
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# Get the percentage of negative tweets
ntweets = df[df.Analysis == 'Negative']
ntweets = ntweets['Tweets']
round((ntweets.shape[0] / df.shape[0]) * 100, 1)
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Plot and visualize the sentiment¶
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#Show the value counts
df['Analysis'].value_counts()
plt.title('Sentiment Analysis')
plt.xlabel('Sentiment')
plt.ylabel('Counts')
df['Analysis'].value_counts().plot(kind='bar')
plt.show()
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