Latent Semantic Analysis Gathering Titles

In order to give an example of Latent Sematic Analysis, I went to Amazon.com, searched on "investing", and took the top 10 books titles that were displayed. One of those book titles only had one index word so I dropped it. Here are the other 9 titles with the index words underlined. To be an index word, the word must occur in 2 or more titles, and not be a "noise" or stop word such as "the", "to", "of", etc.

• The Neatest Little Guide to Stock Market Investing
  
• Investing For Dummies, 4th Edition
  
• The Little Book of Common Sense Investing: The Only Way to Guarantee Your Fair Share of Stock Market Returns
  
• The Little Book of Value Investing
  
• Value Investing: From Graham to Buffett and Beyond
  
• Rich Dad's Guide to Investing: What the Rich Invest in, That the Poor and the Middle Class Do Not!
  
• Investing in Real Estate, 5th Edition
  
• Stock Investing For Dummies
  
• Rich Dad's Advisors®: The ABC's of Real Estate Investing: The Secrets of Finding Hidden Profits Most Investors Miss
  

Latent Semantic Analysis

Latent Semantic Analysis Tutorial

Latent Sematic Analysis or LSA is a way of finding patterns among a collection of documents such as web pages. It is increasingly used by major search engines, such as Google, in ranking websites and determining what AdSense ads to show on a page.

To see how Latent Semantic Analysis works, imagine that you have a collection of documents such as web pages, or in the simple example we show below, book titles. How would you go about finding similarities and differences between the documents?

The Basic Idea

One way is to form a large matrix with each column representing a document, and each row representing a word that has been extracted from the documents. Then, each cell of the matrix is simply the number of times that word appears in that document. For example, if the word "farm" appears in the first document 7 times, then that cell would have a 7 in it. Each cell is simply a count of the number of times that word appears in that document.

The cell numbers are usually massaged, so that whatever patterns are present can be seen more clearly. This step corresponds to "cleaning the data" so that, for example, frequent words are not weighted too heavily, some natural language constructs are simplified, and long documents don't have an unfair advantage. Some ways that cell numbers are massaged are:

• Log of Counts - the log of the counts in each cell may be used instead of the actual counts.
• Stemming - related words may have the same root word and should be considered the same (such as golf and golfing).
• TF-IDF - (term frequency - inverse document frequency) attempts to measure the importance of a term or word.
• Entropy - another way to measure term importance based on the distribution of the term through documents.
• Normalization - sets each document vector to length 1 so documents with more words don't have an unfair advantage.

Road to Web 3.0

Out of all the Internet buzzwords and jargon that have made the transition to the public consciousness, "Web 2.0" might be the best known. Even though a lot of people have heard about it, not many have any idea what Web 2.0 means. Some people claim that the term itself is nothing more than a marketing ploy designed to convince venture capitalists to invest millions of dollars into Web sites. It's true that when Dale Dougherty of O'Reilly Media came up with the term, there was no clear definition. There wasn't even any agreement about if there was a Web 1.0.

Other people insist that Web 2.0 is a reality. In brief, the characteristics of Web 2.0 include:

• The ability for visitors to make changes to Web pages: Amazon allows visitors to post product reviews. Using an online form, a visitor can add information to Amazon's pages that future visitors will be able to read.
• Using Web pages to link people to other users: Social networking sites like Facebook and MySpace are popular in part because they make it easy for users to find each other and keep in touch.
• Fast and efficient ways to share content: YouTube is the perfect example. A YouTube member can create a video and upload it to the site for others to watch in less than an hour.
• New ways to get information: Today, Internet surfers can subscribe to a Web page's Really Simple Syndication (RSS) feeds and receive notifications of that Web page's updates as long as they maintain an Internet connection.
• Expanding access to the Internet beyond the computer: Many people access the Internet through devices like cell phones or video game consoles; before long, some experts expect that consumers will access the Internet through television sets and other devices.

Think of Web 1.0 as a library. You can use it as a source of information, but you can't contribute to or change the information in any way. Web 2.0 is more like a big group of friends and acquaintances. You can still use it to receive information, but you also contribute to the conversation and make it a richer experience.

While there are still many people trying to get a grip on Web 2.0, others are already beginning to think about what comes next. What will Web 3.0 be like? How different will it be from the Web we use today? Will it be a revolutionary shift, or will it be so subtle that we won't even notice the difference?

What do Internet experts think the next generation of the World Wide Web will be like? Keep reading to find out.

Web 3.0 Will Work

Internet experts believe the next generation of the Web -- Web 3.0 -- will make tasks like your search for movies and food faster and easier. Instead of multiple searches, you might type a complex sentence or two in your Web 3.0 browser, and the Web will do the rest. In our example, you could type "I want to see a funny movie and then eat at a good Mexican restaurant. What are my options?" The Web 3.0 browser will analyze your response, search the Internet for all possible answers, and then organize the results for you.

That's not all. Many of these experts believe that the Web 3.0 browser will act like a personal assistant. As you search the Web, the browser learns what you are interested in. The more you use the Web, the more your browser learns about you and the less specific you'll need to be with your questions. Eventually you might be able to ask your browser open questions like "where should I go for lunch?" Your browser would consult its records of what you like and dislike, take into account your current location and then suggest a list of restaurants. ­

To understand where the Web is going, we need to take a quick look at where it's been. Keep reading for a quick lesson on the evolution of the Web.