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Twitter sentiment analysis

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Twitter sentiment analysis and visualizations

Published in: Data & Analytics
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Twitter sentiment analysis

  1. 1. TWITTER SENTIMENT ANALYSIS HARSHIT SANGHVI
  2. 2. DATA COLLECTION AND PREPROCESSING • 27 million tweets (180GB) • Collected in a span of ~1 week (05/05/2015 to 05/09/2015) • Using Java program running on Amazon EC2 • Stored into MongoDB on Amazon EC2 • Cleaning up text of the tweets • Punctuations, numbers, small words, remove stop words • Filter tweets • In non-English language • Without location data
  3. 3. SENTIMENT ANALYSIS • Create Sentiment Prediction model using • Opinion Lexicon (http://www.cs.uic.edu/~liub/FBS/opinion-lexicon-English.rar) • Using Movie Review Dataset (http://ai.stanford.edu/~amaas/data/sentiment)
  4. 4. USING KNIME
  5. 5. VISUALIZATIONS
  6. 6. TWEETS PER DAY PER HOUR
  7. 7. TOP 10 MOST USED HASHTAGS • Shows most commonly discussed topic on twitter
  8. 8. TOP 5 MOST POPULAR USERS
  9. 9. WORD FREQUENCY • Showing words with frequency > 500 and sorted Alphabetically
  10. 10. WORD ASSOCIATIONS • E.g. “Day” appears more with “Mother” and “Happy” and “Birthday”.
  11. 11. LETTER FREQUENCY
  12. 12. # OF WORDS BY LETTER FREQUENCY
  13. 13. LETTER POSITION HEATMAP
  14. 14. SENTIMENT TIMELINE
  15. 15. PRESENTATION USING SHINY
  16. 16. WORD CLOUD
  17. 17. NEGATIVE TWEETS
  18. 18. POSITIVE TWEETS
  19. 19. REFERENCES • Opinion Lexicon (http://www.cs.uic.edu/~liub/FBS/opinion-lexicon-English.rar) • Using Movie Review Dataset (http://ai.stanford.edu/~amaas/data/sentiment) • Twitter Data Mining & Visualizations (http://bit.ly/twtvis) • R Studio (https://www.rstudio.com) • Sentiment Analysis using KNIME (http://www.knime.org/blog/sentiment- analysis)

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