Why Semantic Analysis trumps Sentiment Analysis
Paul Dunay is an award-winning B2B marketing expert with more than 20 years’ success in generating demand and creating awareness for leading technology, consumer products, financial services and professional services organizations.
Paul is the global vice president of marketing for Maxymiser a leading web optimization firm, and author of four “Dummies” books: Facebook Marketing for Dummies (Wiley 2009), Social Media and the Contact Center for Dummies (Wiley Custom Publishing 2010), Facebook Advertising for Dummies (Wiley 2010) and Facebook Marketing for Dummies 2nd Edition (Wiley 2011).
His unique approach to marketing has led to recognition of Paul as a BtoB Magazine Top 25 B2B Marketer of the Year for 2010 and 2009 and winner of the DemandGen Award for Utilizing Marketing Automation to Fuel Corporate Growth in 2008. He is also a finalist for the last six years in a row in the Marketing Excellence Awards competition of the Information Technology Services Marketing Association (ITSMA), and is a 2010 and 2005 gold award winner in Driving Demand.
Buzz Marketing for Technology, Paul’s blog, has been recognized as a Top 20 Marketing Blog for 2009 and 2008, a Top Blog to Watch for 2009 and 2008, and an Advertising Age Power 150 blog in the “Daily Ranking of Marketing Blogs.”
Paul has shared his marketing thought leadership as a featured speaker for the American Marketing Association, BtoB Magazine, CMO Club, MarketingProfs, Marketing Sherpa, Marketing Executives Networking Group (MENG), and ITSMA. He has appeared on Fox News, and his articles have been featured in BusinessWeek, The New York Times, BtoB Magazine, MarketingProfs and MarketingSherpa.
Paul holds an Executive Certificate in Strategy and Innovation from MIT’s Sloan School of Management and a bachelor’s degree in Marketing and Computer Science from Ithaca College.
Paul, in my opinion, any decent sentiment analysis applies semantic analysis. Further, in my opinion, the statement “Simply stated, all methods of sentiment analysis rely on example data” is simply wrong. Many sentiment-analysis methods rely on linguistic artifacts — lexicons of words that indicate subjectivity or sentiment and syntax patterns that link sentiment to subject — in addition to providing scores that aggregate measured sentiment. Better methods will allow arbitrary sentiment classification, not only into positive/negative/neutral tone categories but also into emotion categories (e.g., angry/happy/sad) and intent indicator categories (e.g., plans to renew service/plans to cancel/upgrade candidate).
There’s no opposition between the two categories, sentiment analysis and semantics analysis.
Seth, http://twitter.com/sethgrimes
Seth
First off thanks so much for commenting on my blog – thats huge for me!
here are some thoughts back regarding your comments
regarding this quote “”Simply stated, all methods of sentiment analysis rely on example data” is simply wrong. Many sentiment-analysis methods rely on linguistic artifacts”
Yes, but how do you know if those methods work or not? The only way to know is to use example data to test your system. This is precisely why the paper states “All methods of sentiment analysis rely on example data to design, TEST OR VALIDATE the analysis.” Without example data, you are just guessing at a solution to some unknown problem. And, as soon as you use example data, you run into the statistical confidence problem detailed in the paper.
“in my opinion, any decent sentiment analysis applies semantic analysis”
I don’t disagree. In fact, I would go a step further (as did the paper) and say that sentiment analysis IS semantic analysis. It’s just a form of semantic analysis that has a very narrow view of meaning and relies on very noisy data (as measured by statistical confidence).
and regarding …
“Sentiment analysis is not inherently bad; for particular types of questions, it may be the right tool. But if you use it, make sure the data underlying the analysis is sound and valuable data is not being ignored.”
When sentiment analysis is the right tool for the job, we use it. The important thing is to understand WHEN it is the right tool for the job, which involves understanding its problems, and to understand what the alternatives are.
thanks again and let me know if you want to speak live!
p
Paul, I’d say the word “example” threw me off, but so tell me, how do you “test or validate” semantic analysis other than with a) some form of “gold standard” (example) data, whether machine or human annotated, or b) inter-annotator comparisons, again whether you’re comparing to human or machine annotation that, in this second instance, is not considered “gold standard” definitive?
I can’t think of any way than my (a) and (b), and if you can’t put forward of any other way, then any complaint about the use of “example” data to test or validate sentiment analyses would equally apply to semantic analyses.