Sentiment Analysis and Applications in the News and Media Industry
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Robin Leonard CEO, AllFamous Digital robin_allfamous robinleonard1 SENTIMENT ANALYSIS and applications in the news and media industry
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This is the talk I gave to the Financial Times on 12 September 2013 on Sentiment Analysis and applications in the news and media industry.
Transcript of Sentiment Analysis and Applications in the News and Media Industry
Robin Leonard CEO, AllFamous Digital
robin_allfamous
robinleonard1
SENTIMENT ANALYSIS and applications in the news and media industry
What is the ROI of your mother?
http://www.youtube.com/watch?v=xZY5b85KoOU
Agenda
1. Why Analyze Sentiment? 2. Sentiment Analysis 101 3. Practical Applications
Why Analyze Sentiment?
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
Social Media creates lots of data
Just waiting to become useful information
Consumers are FINALLY empowered
The Anatomy of a Crisis – The Incident
The Anatomy of a Crisis – The Impact
The Anatomy of a Crisis – The Response
The Anatomy of a Crisis – The Result
http://adage.com/article/digital/mckinsey-finds-social-buzz-affect-sales-negatively/242039/
Yes, Sentiment Impacts Sales
Agenda
1. Why Analyze Sentiment? 2. Sentiment Analysis 101 3. Practical Applications
What is Sentiment?
What is Sentiment?
emotions opinions intent
Subjective impressions, not facts
Example 1: Happy Mention
subject emotion brand
product
service
Example 2: Unhappy Mention
fact
fact
product
opinion
emotion
Example 3: Sarcastic Mention
product
brand brand
opinion service
emotion
Example 4: Mixed Mention
product
emotion service brand opinion
Example 5: Language Confusion
product
emotion service
brand opinion
fact
Example 6: Intent to Buy
product
emotion service
fact brand subject
intent
brand brand
question
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
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.
Agenda
1. Why Analyze Sentiment? 2. Sentiment Analysis 101 3. Practical Applications
Use Case 1: Get to the heart of the News
In real time listen for spikes in mention volumes to identify the latest news stories
Use Case 2: Faster Content Crowdsourcing
Once there is a breaking story, quickly identify all the images, videos and public
created content on a topic
Use Case 3: Opinion Mining
Use Case 4: Reputation Management
Use Case 5: Consumer Insights
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”
Use Case 7: Brand Monitoring
Social Media sentiment is the #nofilter voice of the people
Conclusion
Are you listening?
Thank You!!
www.allfamous.com
Robin Leonard CEO, AllFamous Digital
robin_allfamous
robinleonard1
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/
