For a start, install the following pachages:

📦 Tweepy 📦 json

📦 pandas 📦 matplotlib

📦 seaborn 📦 re

📦 nltk

I’ll be using Health_X’s tweets for the purpose of this analysis.

Health_X is a dynamic platform for targeted and impactful dialogue on emerging healthcare & related topics - Adapted from their Twitter Page

Load tweets into a json file

import tweepy
import config
import json
from tweepy import OAuthHandler

auth = OAuthHandler(config.ckey,config.csecret)
auth.set_access_token(config.atoken,config.asecret)
api = tweepy.API(auth)

num_tweets = 1000                    # number of tweets to retrieve
twitter_id = 'healthx_africa'     # userid

file_name = 'healthXtweets.json'
output = open(file_name,"w")
for tweet in tweepy.Cursor(api.user_timeline, id=twitter_id).items(num_tweets):
    output.write(json.dumps(tweet._json)+'\n')
output.close()

Parse json into a dataframe

import json
import pandas as pd

def load_tweets(file):
    with open(file,'r') as f:
        tweets = (json.loads(line) for i, line in enumerate(f.readlines()))
    return tweets

# Select the attributes needed
data = {'text': [], 'created_at': [], 'retweet_count': [], 'favorite_count': [], 'followers_count': []}

tweets = load_tweets(file_name)
for t in tweets:
    data['text'].append(t['text'])
    data['retweet_count'].append(t['retweet_count'])
    data['created_at'].append(t['created_at'])
    data['favorite_count'].append(t['favorite_count'])
    data['followers_count'].append(t['user']['followers_count'])

df = pd.DataFrame(data)
df['created_at'] = pd.to_datetime(df['created_at']) # convert to datetime data type
df.sort_values(by='created_at');

Looking at Favorite and Retweet Counts

import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')

% matplotlib inline
plt.style.use('ggplot')

df_stacked = df.loc[:,('favorite_count','retweet_count')]

fig, axes = plt.subplots(1, 2, figsize=(15,5))
num_days = (max(df['created_at']) - min(df['created_at'])).days
num_months = int(num_days/30)

# default grid appearance
axes[0].plot(df['created_at'],df['favorite_count'],label='favorite count')
axes[1].plot(df['created_at'],df['retweet_count'],label='retweet count',ls="--",color='b')

axes[0].set_title(('Favorite Count over ' + str(num_months) + ' months'))
axes[1].set_title(('Retweets over ' + str(num_months) + ' months'))

axes[0].grid(True)
axes[1].grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)

axes[0].yaxis.set_label_text('Count')
axes[1].yaxis.set_label_text('Count')
axes[0].legend('favorites')
axes[1].legend('retweets')
plt.tight_layout()

HealthX_Africa’s top 10 tweets

top_ten_tweets = df['favorite_count'].sort_values(ascending=False).head(10) # returns a df with first column = index, second column = tweet

print(twitter_id + "'s top 5 tweets (using favorite count)" + "\n")
for i in range(5):
    index = top_ten_tweets.index[i]
    print(str(i+1) + ")" + df.iloc[index]['text'])
    print("Favorite count: " + str(df.iloc[index]['favorite_count']) + "\n")

healthx_africa's top 5 tweets (using favorite count)

1)now counting hours healthx meet panelist i interested key drivers lasting health chang
Favorite count: 12

2)auditing innovationsforhealth important it goes without saying people using
Favorite count: 10

3)are developing innovationsforhealth interested showcasing there opportunity n
Favorite count: 8

4)despite benefits bigdata iot etc make decisions use information data
Favorite count: 8

5)if serious uhc mobilising domestic resources uhc critical im kenyans driven
Favorite count: 6

Word Frequency

Remove Stop words and RT

import re
from nltk.corpus import stopwords
stop = stopwords.words('english')

def text_process(title):
    """"
    1.remove punc
    2.remove stopwords
    3.return array of cleaned text.
    """
    nopunc = re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)|(http.?://[^\s]+[\s]?)"," ",title).split()
    clean_txt = ' '.join([word for word in nopunc if word not in stopwords.words('english')]).lower()

    return clean_txt

df['text'] = df['text'].apply(text_process)

word_series = pd.Series(' '.join(df['text']).lower().split())
top_freq = word_series.value_counts()[:5]

Plot Most frequent Words

plt.figure(figsize=(15,5))
plt.title("HealthX_Afica's favorite words")

top_freq.plot(kind='bar',color='teal',alpha=0.75, rot=0)                            
plt.ylabel('Frequency of Words')
plt.show()

Account Activity

ind_df = df.set_index('created_at')
ind_df = ind_df.groupby(pd.TimeGrouper(freq='W')).count().loc[:,'text'] # group by weeks, count, and extract a series
trim_df = ind_df.iloc[1:len(ind_df)-1] # trim first/last week to ensure we show data collected from full weeks

plt.figure(figsize=(15,5))
plt.title(twitter_id + "'s Account Activity")
plt.xlabel('Date')
plt.ylabel('Number of Tweets')
plt.plot(trim_df)
plt.show()