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Monthly Archives: January 2012
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