initial commit

complaintbox_appV1.py

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+
+
+import tweepy
+import time
+import pandas as pd
+from transformers import pipeline
+import matplotlib.pyplot as plt
+import gradio as gr
+
+def twitter_auth(consumerkey,consumersecret):
+  consumer_key = consumerkey
+  consumer_secret = consumersecret
+
+  auth = tweepy.AppAuthHandler(consumer_key,consumer_secret)
+
+  api = tweepy.API(auth,wait_on_rate_limit= True,wait_on_rate_limit_notify=True)
+  return api
+
+"""## Helper function for handling ratelimit and pagination"""
+
+def limit_handled(cursor):
+  """
+  Function takes the cursor and returns tweets
+  """
+  while True:
+    try:
+      yield cursor.next()
+    except tweepy.RateLimitError:
+      print('reached rate limit, sleeping for > 15 mins')
+      time.sleep(15*61)
+    except StopIteration:
+      break
+
+
+
+def tweets_collector(query,count):
+  api = twitter_auth(consumerkey,consumersecret)
+  query = query +' -filter:retweets'
+  search = limit_handled(tweepy.Cursor(api.search,q = query,tweet_mode = 'extended',lang ='en',result_type ='recent').items(count))
+  sentiment_analysis = pipeline(model = "finiteautomata/bertweet-base-sentiment-analysis")
+  tweets = []
+
+  for tweet in search:
+    try:
+      content = tweet.full_text
+      sentiment = sentiment_analysis(content)
+      tweets.append({'tweet' : content ,'sentiment': sentiment[0]['label']})
+    except:
+      pass
+  return tweets
+
+"""## Run sentiment Analysis"""
+
+#tweets = tweets_collector(query,count)
+#df = pd.DataFrame(tweets)
+
+import pandas as pd
+
+pd.set_option('max_colwidth',None)
+pd.set_option('display.width',3000)
+
+#import matplotlib.pyplot as plt
+
+#sentiment_counts = df.groupby(['sentiment']).size()
+
+#fig = plt.figure(figsize = (6,6),dpi = 100)
+#ax = plt.subplot(111)
+#sentiment_counts.plot.pie(ax = ax,autopct = '%1.f%%',startangle = 270,fontsize = 12,label = "")
+
+def complaint_analysis(query,count):
+  tweets = tweets_collector(query,count)
+  df = pd.DataFrame(tweets)
+  from wordcloud import WordCloud
+  from wordcloud import STOPWORDS
+  sentiment_counts = df.groupby(['sentiment']).size()
+  fig = plt.figure(figsize = (6,6),dpi = 100)
+  ax = plt.subplot(111)
+  sentiment_counts.plot.pie(ax = ax,autopct = '%1.f%%',startangle = 270,fontsize = 12,label = "")
+  plt.savefig('Overall_satisfaction.png')
+
+  positive_tweets = df['tweet'][df['sentiment'] == 'POS']
+  stop_words = ["https","co","RT","ola_supports","ola_cabs","customer"] + list(STOPWORDS)
+  positive_wordcloud = WordCloud(max_font_size=50,max_words = 30,background_color="white",stopwords=stop_words).generate(str(positive_tweets))
+  plt.figure()
+  plt.title("Positive Tweets - Wordcloud")
+  plt.imshow(positive_wordcloud,interpolation="bilinear")
+  plt.axis("off")
+  #plt.show()
+  plt.savefig('positive_tweet.png')
+  negative_tweets = df['tweet'][df['sentiment'] == 'NEG']
+  stop_words = ["https","co","RT","ola_supports","ola_cabs","customer"] + list(STOPWORDS)
+  negative_wordcloud = WordCloud(max_font_size=50,max_words = 30,background_color="white",stopwords=stop_words).generate(str(negative_tweets))
+  plt.figure()
+  plt.title("Negative Tweets - Wordcloud")
+  plt.imshow(negative_wordcloud,interpolation="bilinear")
+  plt.axis("off")
+  #plt.show()
+  plt.savefig('negative_tweet.png')
+  return ['Overall_satisfaction.png','positive_tweet.png','negative_tweet.png']
+
+gr.Interface(fn=complaint_analysis, 
+             inputs=[
+            gr.inputs.Textbox(
+                placeholder="Tweet handle ples", label="Company support Twitter Handle", lines=5), gr.Slider(100, 1000) ],
+             outputs= [gr.outputs.Image(type="pil"),gr.outputs.Image(type="pil"),gr.outputs.Image(type="pil")],
+             examples=[]).launch(debug= True)
+
+
+
+
+