Twitter sentiment analysis and visualizations

1. TWITTER SENTIMENT
ANALYSIS
HARSHIT SANGHVI

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. 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. USING KNIME

5. VISUALIZATIONS

6. TWEETS PER DAY PER
HOUR

7. TOP 10 MOST USED
HASHTAGS
• Shows most commonly discussed
topic on twitter

8. TOP 5 MOST
POPULAR USERS

9. WORD FREQUENCY
• Showing words with frequency
> 500 and sorted
Alphabetically

10. WORD
ASSOCIATIONS
• E.g. “Day” appears more with
“Mother” and “Happy” and
“Birthday”.

11. LETTER FREQUENCY

12. # OF WORDS BY
LETTER FREQUENCY

13. LETTER POSITION
HEATMAP

14. SENTIMENT TIMELINE

15. PRESENTATION USING SHINY

16. WORD
CLOUD

17. NEGATIVE
TWEETS

18. POSITIVE
TWEETS

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)