Text Mining in Hotel Reviews: Bally’s vs. Bellagio

  2 m, 24 s

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 consensus 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.

Categories: Natural Language Processing, Sentiment Analysis, Text Analytics