Analysed Sentiment and Plotted a plot

1 files changed, 38 insertions, 11 deletions

diff --git a/SentimentAnalyser.py b/SentimentAnalyser.py
index ba415dc..48f3432 100644
--- a/SentimentAnalyser.py
+++ b/SentimentAnalyser.py
@@ -1,6 +1,7 @@
 from nltk.sentiment.vader import SentimentIntensityAnalyzer
 from cassandra.cluster import Cluster
 import re
+import pandas as pd
 
 
 compound = []
@@ -10,17 +11,43 @@ neg = []
 
 def sentence_score(rs):
     review_score = SentimentIntensityAnalyzer()
-    compound.append(review_score.polarity_scores(rs)['compound'])
-    neg.append(review_score.polarity_scores(rs)['neg'])
-    neu.append(review_score.polarity_scores(rs)['neu'])
-    pos.append(review_score.polarity_scores(rs)['pos'])
+    return review_score.polarity_scores(rs)['compound']
+    # compound.append(review_score.polarity_scores(rs)['compound'])
+    # neg.append(review_score.polarity_scores(rs)['neg'])
+    # neu.append(review_score.polarity_scores(rs)['neu'])
+    # pos.append(review_score.polarity_scores(rs)['pos'])
 
 
-cluster = Cluster()
-session = cluster.connect('twitter')
-rows = session.execute('SELECT tweet FROM twitterdata')
-for tweet in rows:
+cluster = Cluster(['127.0.0.1'], port=9042)
+session = cluster.connect()
+session.set_keyspace('twitter')
+session.execute("USE twitter")
+
+# Select all tweets from cassandra database
+query = "SELECT * FROM twitterdata"
+rows = session.execute(query)
+tweets = []
+
+# Iterate through all tweets
+for row in rows:
     try:
-        sentence_score(tweet)
-    except re.error:
-        print(tweet)
\ No newline at end of file
+        tweets.append({
+            'tweet_id': row.tweet_id,
+            'tweet': row.tweet,
+            'score': sentence_score(row.tweet)
+        })
+    except:
+        print(row.tweet)
+
+
+for tweet in tweets:
+    if tweet.get('score') > 0.5:
+        tweet['sentiment'] = 'positive'
+    elif tweet.get('score') < -0.5:
+        tweet['sentiment'] = 'negative'
+    else:
+        tweet['sentiment'] = 'neutral'
+
+df = pd.DataFrame(tweets)
+df.to_csv('tweets.csv', index=False)
+