Sales of U.S. Existing Homes Increase as Market Stabilizes – Bloomberg – Gains in employment, depressed prices and record-low mortgage rates may bring more properties within reach of buyers, eliminating a source weakness for the world’s largest economy just as risks from Europe’s debt crisis climb. At the same time, efforts to reduce foreclosures and free up financing are just beginning to take root, signaling a sustained housing recovery will take time to develop.- AT LEAST TWO MORE YEARS OF TEPID REAL ESTATE ACTIVITY IF YOU ASK ME.
Analyze Words – What does AnalyzeWords do?
AnalyzeWords helps reveal your personality by looking at how you use words. It is based on good scientific research connecting word use to who people are. So go to town – enter your Twitter name or the handles of friends, lovers, or Hollywood celebrities to learn about their emotions, social styles, and the ways they think. – IT CRASHES FOR @NEURALMARKET. HMMMM.
The Aleph Blog » Blog Archive » Why Amateurs Should Invest in Common Stocks – Now, some of the successes came with failures. For a while, I told my kids never to mention the name “Caldor” to me. Yeh, Michael Price may have lost a billion on that one, but I more than took my licks. Until you lose a decent amount, you don’t really understand how the market works. You can call it market tuition, but like tuition at college, you don’t know how much value you will get out of what you have paid.
Smart money left silver to tarnish retail buyers – MarketWatch – The trend suggests the so-called ’smart money,’ the large managed funds that report to the CFTC, had started to back away from silver and “retail investors picked up the slack,” said Tom Pawlicki, a precious metals analyst with MF Global in Chicago. – HOW TRUE BASED ON MY EXPERIENCE AT THE COIN SHOW.
Judge Rejects Google’s Deal to Digitize Books – NYTimes.com – The deal would have allowed Google to make millions of out-of-print books broadly available online and to sell access to them, while giving authors and publishers new ways to earn money from digital copies of their works. Yet the deal faced a tidal wave of opposition from Google rivals like Amazon and Microsoft, as well as some academics, authors, legal scholars, states and foreign governments. The Justice Department also opposed the deal, fearing that it would give Google a monopoly over millions of so-called orphan works, books whose right holders are unknown or cannot be found.
First North Pole Ozone Hole Forming? – The cold snap is no coincidence, research leader Rex added."This is the continuation of a long-term tendency that the cold Arctic winters have become colder," Rex said.And global warming may drive this trend, he added. As greenhouse gases trap heat in the lower levels of the atmosphere, the higher levels tend to cool, he said.Of course, the "process is more complicated than this simple explanation"—there may be many ways in which greenhouse gases influence high-altitude temperatures, he added. – WE BETTER MAKE CHANGES FAST!
I’ve been traveling a lot lately and managed to catch up on a bit of reading when I’m crusing at 30,000 feet. On my nook right now is a fascinating book that all text miners should at least browse in a book store. It’s called “The Secret Life of Pronouns,” by James Pennebaker.
The premise of the book is that your social status, sex, personality, and secret intentions can be determined by analyzing pronouns (I, you, they), artciles (a, an, the), and few other functional words. In the beginning of his research, James used the Liguisitic Inquiry and Word Count (LIWC) program but appears to have modified it with proprietary word dictionaries.
From the surface, LIWC looks similar to the word frequency routine that Rapidminer does in the Process Documents operator, but they went further and added a bit more “intelligence” to the analysis. What they did was roll out a fun servce called Analyze Words. You just enter your Twitter handle, click the button, and it gives you a snapshot into your tweet sentiment.
So how does this work? I suspect that James and team use their dictionaries to categorize incoming text documents and test against them and for the author’s sex, social status, personality, and sentiment. I’m sure that a lot of “up front” and hard work was done to build these dictionaries. A lot of “up front” work is the norm with text mining and if you try using short cuts, you’ll likely get crappy models.
I think a model like his can be done quite easily in Rapidminer, especially if you build a good crawling and sentiment system to test against. All that it requires is a bit of thought and the will to do it. Isn’t the data driven world we live in, cool?