My presentation at #ThinkPH 'The Internet, Big Data and You' Conference, on August 23, 2013 at New World Hotel, Makati. Click here to see the #ThinkPH conference details and agenda: http://www.rappler.com/bulletin-board/36539-agenda-rappler-google-thinkph-internet-big-data-conference Event hosted by Rappler, Google and SocialGood. My slides cover: 1. Why analyze sentiment? 2. How does sentiment analysis work? 3. Practical applications 4. Sentiment of #ThinkPH Conference

1. Presents to
Robin Leonard
CEO, AllFamous Digital
robin_allfamous
robinleonard1
SENTIMENT ANALYSIS
Click here to see the agenda and conference details

2. Agenda
1. Why Analyze Sentiment?
2. Sentiment Analysis 101
3. Practical Applications
4. Sentiment of #ThinkPH

3. Agenda
1. Why Analyze Sentiment?
2. Sentiment Analysis 101
3. Practical Applications
4. Sentiment of #ThinkPH

4. Why Analyze Sentiment?

5. Emotions are core to purchase decisions
vs.
http://www.today.mccombs.utexas.edu/2010/04/do-you-make-buying-decisions-based-on-logic-
or-emotion-a-tale-of-two-chickens

6. Social Media creates lots of data
Just waiting to become useful information

7. Consumers are FINALLY empowered

8. The Anatomy of a Crisis – The Incident

9. The Anatomy of a Crisis – The Impact

10. The Anatomy of a Crisis – The Response

11. The Anatomy of a Crisis – The Result

12. http://adage.com/article/digital/mckinsey-finds-social-buzz-affect-sales-negatively/242039/
Yes, Sentiment Impacts Sales

13. Agenda
1. Why Analyze Sentiment?
2. Sentiment Analysis 101
3. Practical Applications
4. Sentiment of #ThinkPH

14. What is Sentiment?

15. What is Sentiment?
emotions opinions intent
Subjective impressions, not facts

16. Example 1: Happy Mention
subject emotion brand
product
service

17. Example 2: Unhappy Mention
fact
fact
opinion
emotion

18. Example 3: Sarcastic Mention
brand brand
opinion service
emotion

19. Example 4: Mixed Mention
emotion service brand opinion

20. Example 5: Language Confusion
brand opinion
fact

21. Example 6: Intent to Buy
fact brand subject
intent
brand brand
question

22. The elusive hunt for binary
• For vs. Against
• Like vs. Dislike
• Good vs. Bad
• Ecstatic vs. Happy
• Excited vs. Amused
• ROFL vs. LOL
• Depressed vs. Unhappy
• Sad vs. Destitute
• Frustrated vs. Angry

23. Challenges of Automated Sentiment Analysis
§ People express opinions in
complex ways
§ Cultural ideosyncracies
(e.g. differences in sarcasm,
irony etc)
§ The wider you throw your net
and the more complex the
language, the less accurate
the system will be.

24. Agenda
1. Why Analyze Sentiment?
2. Sentiment Analysis 101
3. Practical Applications
4. Sentiment of #ThinkPH

25. Use Case 1: Social Focus Group
“Coffee in a can”
“Iced Coffee”

26. Use Case 2: Sales & Service

27. Use Case 3: Opinion Mining

28. Use Case 4: Reputation Management

29. Use Case 5: Consumer Insights

30. Use Case 6: Competitive Intelligence
Get real-time
insights on your
competitors
- Sun Tzu
“Know your enemy and know
yourself and you can fight a
hundred battles without disaster”

31. What is the Sentiment of #ThinkPH?
There were 4,627 mentions of #ThinkPH on 23 August 2013

32. What did people say about the #ThinkPH?
(on a side note)
Mentions of #thinkPH Mentions of Robin Leonard + #thinkPH

33. Thank You!!
www.allfamous.com
Robin Leonard
CEO, AllFamous Digital
robin_allfamous
robinleonard1

34. References
§ http://www.lct-master.org/files/MullenSentimentCourseSlides.pdf
§ http://www.slideshare.net/mcjenkins/how-sentiment-analysis-
works#btnNext
§ http://en.wikipedia.org/wiki/Sentiment_analysis
§ http://mashable.com/2010/04/19/sentiment-analysis/
§ http://www.today.mccombs.utexas.edu/2010/04/do-you-make-
buying-decisions-based-on-logic-or-emotion-a-tale-of-two-chickens
§ http://adage.com/article/digital/mckinsey-finds-social-buzz-affect-
sales-negatively/242039/