[+] Create visualization script

src/process/twitter_visualization.py

@@ -0,0 +1,48 @@
+from matplotlib import pyplot as plt
+from tabulate import tabulate
+
+from process.twitter_process import *
+
+
+def view_covid_tweets_freq(users: list[ProcessedUser],
+                           sample_name: str,
+                           tweets_dir: str = './data/twitter/user-tweets/'):
+    tweets_dir = normalize_directory(tweets_dir)
+
+    # Load tweets, and get the frequency of covid tweets for each user
+    user_frequency = []
+    for u in users:
+        # Load processed tweet
+        tweets = load_tweets(tweets_dir, u.username)
+        # Get the frequency of COVID-related tweets
+        freq = len([1 for t in tweets if t.covid_related]) / len(tweets)
+        user_frequency.append((u.username, freq))
+
+    # Sort by frequency
+    user_frequency.sort(key=lambda x: x[1], reverse=True)
+
+    # How many people didn't post about COVID?
+    print(f"In {sample_name} -")
+    print("How many people didn't post about COVID:",
+          len([a for a in user_frequency if a[1] == 0]))
+    print("How many people have less than 1% of their posts about COVID:",
+          len([a for a in user_frequency if a[1] <= 0.01]))
+    print()
+
+    # Top 20
+    print(f"20 Users of who post COVID-related tweets most frequently:")
+    print(tabulate([[u[0], f'{u[1] * 100:.1f}%'] for u in user_frequency[:20]],
+                   ['Username', 'Frequency']))
+
+    # Graph histogram
+    plt.title(f'COVID-related posting frequency for {sample_name}')
+    plt.xticks(rotation=90)
+    plt.tight_layout()
+    plt.hist([f[1] for f in user_frequency], bins=100, color='#ffcccc')
+    plt.show()
+
+
+if __name__ == '__main__':
+    sample = load_user_sample()
+    view_covid_tweets_freq(sample.most_popular, '500 most popular Twitter users')
+    view_covid_tweets_freq(sample.random, '500 random Twitter users')