Create sentiment_analysis.py

market_analysis/sentiment_analysis.py

@@ -0,0 +1,26 @@
+import tweepy
+import nltk
+from nltk.sentiment import SentimentIntensityAnalyzer
+from textblob import TextBlob
+
+class SentimentAnalysis:
+    def __init__(self, api_key, api_secret, access_token, access_token_secret):
+        self.api_key = api_key
+        self.api_secret = api_secret
+        self.access_token = access_token
+        self.access_token_secret = access_token_secret
+        self.auth = tweepy.OAuthHandler(self.api_key, self.api_secret)
+        self.auth.set_access_token(self.access_token, self.access_token_secret)
+        self.api = tweepy.API(self.auth)
+        self.sia = SentimentIntensityAnalyzer()
+
+    def get_tweets(self, query, count):
+        tweets = tweepy.Cursor(self.api.search, q=query, lang='en', tweet_mode='extended').items(count)
+        return [tweet.full_text for tweet in tweets]
+
+    def analyze_sentiment(self, tweets):
+        sentiments = []
+        for tweet in tweets:
+            blob = TextBlob(tweet)
+            sentiments.append(self.sia.polarity_scores(tweet)['compound'])
+        return sentiments