Hotel Reviews represent one of my favorite uses of text analytics. About five years ago we built a site with FAST that measured hotel reviews to build a “consensus opinion ” of hotels in a narrow geographic area. The idea was to give users of the site (shown below) an idea of what people thought of various hotels in a given area (Manhattan for example). It’s a nice application because it plays to the strengths of sentiment scoring, where a group of reviews are rolled together to form a concensus opinion. Automated engines are very accurate in such a use case (possibly more accurate than people), and they can handle a large volume of content.  

Recently we revisited the scoring of hotel reviews, and dove a bit deeper this time. Rather than simply generating a score for each property we scored the reviews for various features of the hotel, like location and staff and dining. For this test we used reviews for a couple of hotels in Las Vegas, the Bellagio and Bally’s and we measured the following features for each: - Rooms - Price - Facilities - Location - Cleanliness - Service - Overall An important aspect of this analysis is that the hotels are basically in the same location (right across the street from each other). When you examine the results (below), you’ll see that the hotels scored nearlythe same on location. This is a good test that the results are indicative of reality.

Digging deeper into the results, I was surprised to see that Bally’s had higher scores than Bellagio because Bellagio is one of the 5-star properties in Vegas, so we dug a bit deeper to make sure we weren’t scoring the reviews wrong. We focused in on the most positive and most negative reviews and tried to figure out why Bellagio wasn’t scoring higher. The chart below shows that the “happy campers” were equally happy with Bellagio and Bally’s the unhappy visitors were really unhappy with the Bellagio.

When we dug into the reviews we discovered that people expected more for their money than they were getting at Bellagio. Through the simple application of sentiment analysis on publicly available information, we show that companies can make these comparisons with much higher reliability, at minimal incremental cost, and with an unprecedented ability to adjust categories on-the-fly, either based on these results, or to test out new hypotheses. In fact, using this technique, we can move beyond the limitations of traditional approaches by running additional analysis to discover new, previously unmeasured categories based on recurring themes within the data. What this means for brands is that those who are able to leverage sentiment analysis will remain at significant advantage over their competitors, able to anticipate and proactively respond to how customers perceive their brand, much faster, more comprehensively, and significantly cheaper than existing methods.

Entity extraction in text analytics is the basis of the entire process - identify people, places, companies and themes and use them to better understand the content.

There are two types of entity recognizers that we have used in Salience 4 with much success:

Model based entities: this process looks at language and determines factors like parts of speech and extracts the relevant entity.

Customer driven lists: this process looks at a list provided by a customer to match and extract the relevant entity.

Our soon to be released Salience 4.3 is introducing query-based entities. This process takes into account the combination of words to make a match and extracts the entity based on that query.

For example, there is more than one Senator Udall in the United States. Mark Udall is a Senator from Colorado. Tom Udall is a Senator from New Mexico. If you had a query-based entity recognizer for "Tom Udall" you would create a query that includes the terms "Senator Udall" and "New Mexico" to determine that it must be Senator Tom Udall and not Senator Mark Udall.

While this is often compared with confidence-based entities, this isn't based on a confidence about the language, but an absolute.

I came across this great article by Frank Brown Ph. D from Accelrys (a company, I'll be honest, I had never heard of until today) in Information Management. He was describing the world of the scientific enterprise and how smart information management can help to strike a balance between content and context in R&D. In part, he wrote, "The term “business intelligence” has risen in the realm of information management for a reason. A collection of letters, numbers, figures or images are meaningless until processed in a way that makes the information understandable and usable. That’s what distinguishes raw data from true intelligence." I think regardless of industry, people are wondering how to get at silos of information and make them more useful. He continued with, "But when the available knowledge base includes an enormous breadth of sources, data formats and locations, relying on human processing alone is simply not feasible. This is where emerging technologies such as advanced semantic search and text analytics come in. These types of artificially intelligent categorization tools can help remove the time and cost constraints involved in extracting the context from complex content so that research collaborators can capitalize on all the valuable stores of data available to them – structured and unstructured, proprietary and public." If text analytics and business intelligence can help with the information management process for companies whose job it is to better the world with drug manufacturing or scientific break-throughs, what do you think it could do for your company?

For over 7 years we've been known as Lexalytics. I'll be honest, I didn't pick the name so I can't take credit. However, I often get asked what it means or where it came from. To us, it's pretty simple. You take Lexical: Main Entry: lexical Function: adjective Date: 1836 1 : of or relating to words or the vocabulary of a language as distinguished from its grammar and construction 2 : of or relating to a lexicon or to lexicography and add Analytics and you get Lexalytics. Since we analyze text-based content, or words, to provide additional analysis to customers, it was the perfect name. Lately, I've seen a little spike in other "lytics" popping up at conferences and online. For example, Social Analytics. Referred to as Socialytics. Good one. Today I saw Community Analytics - Communilytics. I'm certainly not claiming Lexalytics kicked off the world of "lytics" - that'd be silly - but what does encourage me is the fact that businesses and organizations are investigating different ways to analyze information. There has been a major transition in the past 5 years of content from scanned, stored and printed to blogged, posted and shared. As more and more channels are used to create and disseminate content, enterprises will need to explore all the various ways analytics will play into their infrastructure and applications. Some analytics are based on pages and pages of documents published in very specialized industries while some are comments and posts on very public domains. Whether you are analyzing Pharmaceutical data like our friends at Pharmalytics or simply exploring tweets and social content (Twitterlytics, perhaps?), there is a lot of power and information to be found within content. Try it for yourself. You may find a new form of "lytics" you can share with us all.