-
Recent Posts
- Senti-meter Scans the Twitterverse for Movie Sentiment: Oscar or Runner Up?
- Would Beethoven Have Given a Rat’s Ass about Business Analytics?
- Sentiment Analysis for Business, Finance & Social Media Showcased at May 8, New York Symposium
- Decoding Content at Tech@State: Real Time Awareness
- Smart Content Re-viewed: Text Analytics and Semantic Content Enrichment
Archives
Categories
Meta
Author Archives: Seth Grimes
Senti-meter Scans the Twitterverse for Movie Sentiment: Oscar or Runner Up?
The Academy Awards are next Sunday, their 84th iteration, social-media aware as never before. The Academy invites you to “Join the Conversation” via the #Oscars and #CelebrateTheMovies Twitter hashtags and gives over major Web-page real estate to Oscar Blogs and … Continue reading
Posted in analytics, sentiment analysis, social media
Tagged IBM, Los Angeles Times, USC Annenberg School
1 Comment
Would Beethoven Have Given a Rat’s Ass about Business Analytics?
This posting started off as a comment to Gary Cokins’ SAS blog article, Could Beethoven have implemented business analytics? I decided to share my thoughts more widely however, really reflections about where we in the analytics world came from, and … Continue reading
Posted in BI, analytics
2 Comments
Sentiment Analysis for Business, Finance & Social Media Showcased at May 8, New York Symposium
The speaker line-up next Sentiment Analysis Symposium is out. This symposium, the fourth, is slated for May 8, 2012 in New York. With speakers and panelists leading firms (including American Express, Fidelity Investments, Kraft Foods, the Red Cross, Thomson Reuters, … Continue reading
Posted in sentiment analysis, text analytics
Leave a comment
Decoding Content at Tech@State: Real Time Awareness
I’m moderating the panel this afternoon at the Tech@State conference, convened by the State Department, taking place at George Washington University. We — David Broniatowski (Synexxus), Ravi Patel (Yahoo! Research), Noah Smith (Carnegie Mellon Univ), and V.S. Subrahmanian (Univ of … Continue reading
Posted in Uncategorized
Leave a comment
Smart Content Re-viewed: Text Analytics and Semantic Content Enrichment
A recent blog article of mine (thankfully) gave rise to a number of off-topic comments concerning the meaning of semantic content enrichment. As Marie Wallace of IBM remarked, it’s great to see the term semantic content enrichment generating discussion although … Continue reading
Wisdom of the Sports Crowd: Good Odds with Sentibet Sentiment Analysis
Sentiment analysis is hot among financial-market traders, so why not for that other great betting domain, sports? Serafim Scandalos, an exec at Neurolingo, a Greek natural language processing (NLP) specialist, asked my reactions to Sentibet, currently in beta, which performs … Continue reading
Stephen Arnold Blows a Gasket
Stephen Arnold, a provider of “news and information… about search and content processing,” has his hatchet out in Temis, Spammy PR, and Quite Silly Assertions. Stephen Arnold, “Thy wit is a very bitter sweeting; it is a most sharp sauce,” … Continue reading
Posted in marketing, semantics, text analytics
12 Comments
What are the most powerful open-source sentiment-analysis tools?
I took a stab at a Quora question, What are the most powerful open-source sentiment-analysis tools?. Here’s my response: I know of no open-source (software) tools dedicated to sentiment analysis. Instead, a variety of open-source text-analytics tools — natural-language processing … Continue reading
Text Analytics in 2012
Will 2012 be The Year of Text Analytics? But wait. Wasn’t 2011 — weren’t 2010 and a few years before that — for those in the know? I think so, and I think 2012 will keep up the pace, seeing … Continue reading
Posted in text analytics
Tagged market sizing, question answering, semantic search, semantics, text analytics
1 Comment
From Sentiment Analysis to Enterprise Applications
If your perception of sentiment analysis was shaped by Twitter-sentiment toys, it’s time for a relook. These “toys” simplistically score tweets positive/negative/neutral based solely on keyword presence without regard for context. Semantically rooted sentiment technologies do better by getting at … Continue reading