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Hackathon Highlights: AI and the role of sentiment in location optimization

Started ‎01-20-2020 by
Modified ‎07-28-2020 by
Views 2,517

Location, Location, Location.


It may sound crazy that in today’s digitized world, physical location still matters a great deal to organizations. Think about any real estate business (hotels, restaurants, movie theaters, etc., you name it!)—finding a good location still remains somebody’s full-time job.


DID YOU KNOW? An array of surveys (from HubSpot, TripAdvisor, etc.) has revealed that:SASHackathonHighlights.jpg

  • For restaurants, 1 in 3 customers won’t eat at a restaurant rated only 3 stars.
  • For hospitality, 32% eliminated from consideration those properties with a rating below 4 stars.
  • A one-star increase on Yelp will lead to a 9% increase in restaurant revenue.
  • 96% of travelers consider reviews important when researching a hotel.

As you can see, customers leave their interaction footprint in all shapes and forms across the media. And yes, this is how your customers behave today!


Facing the increasing number of reviews online, it seems inevitable that businesses should consider customer reviews when making any location decisions. However, the question is: HOW should they do so? Text data is unstructured, massive and messy. Collecting, crunching and extracting meaningful insights from text data, then embedding this information into location models, is not an easy task. The good news: it is possible.


Challenge: Strategically locate a new hotel


During the AI Hackathon week in which SAS’ Customer Advisory Academy 5 participated, our group's task was to create a feasible and scalable solution for businesses using SAS’ Artificial Intelligence (AI) capabilities, including text analytics, network analytics, computer vision, recommendation engine, and optimization. Inspired by the potential of text analytics, we decided to take on the challenge. To make our solution more relevant for tangible business value, we picked a fictitious hotel chain as our target client. The business problem: Find a strategic location in the US for the hotel chain to put a brand-new hotel. The hotel’s management also wanted to leverage the growing segment of young, middle-class customers in this expansion.


Our approach: Text analytics with online reviews


To approach this business problem, our group identified that the most important pain point for the client was the need to secure its first-mover advantage. Identifying uptrend locations ahead of the hype was essential to the client’s success. As mentioned above, reviews strongly influence service providers. Therefore, we incorporated review data into our analysis and analyzed how sentiments expressed online affect strategic location decisions. Given the time limit (24 hours), we limited our scope to two locations: Raleigh and Portland. As a result, we ended up with 2,297 reviews across different entertainment types (restaurants, bars and pubs, shopping malls, gyms, theme parks, etc.), which we felt would be appealing to the client’s target customers (young, middle-class Millennials).


We started off by using SAS® Visual Text Analytics (VTA) to identify concepts and topics Millennials most care about and discuss online. Next, we scored customer sentiments on the scale of 1 to 3(Positive, Negative and Neutral). We also used VTA to identify common topics among customer reviews. Below are a few snapshots of our quick VTA effort.


Concept Node: extracts predefined concepts or create additional custom concepts that you can discover in a document or set of documents.Concept Node: extracts predefined concepts or create additional custom concepts that you can discover in a document or set of documents.

Visual Text Analytics model pipeline:SAS Visual Text Analytics provides a number of text analysis pipeline nodes, arranged in a sequence that you control.Visual Text Analytics model pipeline:SAS Visual Text Analytics provides a number of text analysis pipeline nodes, arranged in a sequence that you control.

Diagnostic Metrics for Automatically Generated Categories: shows the F-Measure, Precision, and Recall values for each automatically generated category.Diagnostic Metrics for Automatically Generated Categories: shows the F-Measure, Precision, and Recall values for each automatically generated category.

After calculating sentiment scores for customer reviews both in Portland and Raleigh, we surfaced the insights back to SAS® Visual Analytics to put things into perspective. Via SAS Visual Analytics, we were able to achieve a global overview of the trendiness of each area. Below is our overview dashboard.


dashboard 1.png


The report was interactive and drill-able, allowing us to look deeper into each specific area of focus.


dashboard 2.png



As shown in the graph, when we drilled down into Raleigh, shopping malls seemed to have lower sentiment scores compared to restaurants, fitness centers and cafes. From a business standpoint, putting a hotel near a shopping mall may not be ideal as customer sentiments toward these areas are likely to be more negative, which in turn may affect hotel reviews as a spill-over effect.


We then used distance data (latitude and longitude) to cluster reviews on a geographical scale. Next, we used sentiments to assign weights to the cluster centroids, which are the middle points of each cluster. These weights were calculated by multiplying each cluster’s sentiment score by the average star rating of the corresponding properties and the total number of reviews. We used these weights to adjust the centroids correspondingly. The below visualization shows how the centroids were shifted.


dashboard 3.png


Two pieces of feedback that we received from the judging panel during the AI Hackathon were: 1) Sentiment scores and average star ratings might be correlated, and 2) It might be simpler to use sentiments directly in the clustering model, rather than the weights. We believe that these were excellent ideas, and we would love to explore them more to improve our approach. One possible alternative we may explore is to use sentiment score directly in the clustering model, which would allow us to easily measure the effect of inserting the sentiment scores into a traditional geo-clustering algorithm. The good news is that this can be done smoothly and effortlessly within SAS® Visual Analytics.


So, why don’t you give it a try and let us know if you find something interesting?




Collaborators with @yennguyen_sas on this app and article include: @jmowlai@TylerGillson, and @XinRu.  




I want to collect data from linkedIn, twitter and companies websites (press releases, customer reviews etc.) for my research thesis. I need the coding and identification of themes and patterns from the data set. which specific text mining software I can use for the above mentioned data?


Thank you

Kind Regards,


Hi Shabana,


Are you interested in collecting data from social media and websites for your thesis? If so, you can use Proc HTTP. See a blog about how to scrap data from websites :

For text mining and analytics, you can use SAS Visual Text Analytics to perform preprocessing and analyzing. There is one example about using Instagram feeds for marketing analysis that you can refer to:


I hope you find the above links helpful.



Yen Nguyen

Hi Yen,


Many thanks for your response and links. I really appreciate your assistance. I want to know the SAS softwares that could perform coding and performing a sort of thematic analysis to identify themes. I know some SAS softwares that identify themes on the basis of number of counts and frequencies of a variable in the big data but I need a sort of coding analysis like Nvivo does and I do not need analysis on the basis of number of counts of a variable.


I want to collect information from the different online platforms that includes social networking sites, customer reviews and companies website. Can you identify few SAS softwares that pull the data from different social networking sites and companies website and also SAS softwares for identifying themes like Nvivo does and as well as SAS softwares for the analysis purpose?

That would be good if you could send me the possible list of softwares that meet my research needs as mentioned above.


Thank you,

Kind Regards,


Hi Shabana,


As per my understanding, there are 2 things that you are looking for:

1. Theme/Topic detection for text data: For this, you can have a look at SAS Visual Text Analytics product that we have ( This product offers a user-friendly interface that allows you to define rules and generate linguistic code for you (quite similarly to Nvivo). Topic or theme detection is enabled based on the co-occurrence of different bags of words. You can also define a theme of your interest if you have any in mind. Both out-of-the-box options and custom options are made accessible for you from the visual. I would encourage you to have a look at this tutorial video: and this webinar: to learn more about the product and see if it fits your needs. 


2. Data scrapping from social networking sites: SAS used to have a built-in utility for scraping data from social sites. However as currently, many social sites have limited their portal to third-party data collectors, we are now no longer able to leverage the built-in utility and I would assume other third-party data collectors would be affected by this new change. Fortunately, because SAS provides API communication, if you have a social account with API key and secrets, you can still scrape data from these sites using SAS. For this, I would refer to the document that I sent in my earlier message:


I hope my information is helpful for you and your research. Please feel free to reach out if you need any further help.


Yen Nguyen

Hi Yen,

Many thanks for your assistance. Can you please provide any material related to how we can make define themes using SAS visual text Analytics. Thanks for the links that you sent but I need some more material with example that how exactly we can define our themes. Or if you know of any research article that has used SAS visual text Analytics. You can provide me the name of those research articles or link, either is ok with me. Further, can we perform sentiment analysis through this software too?


Furthermore. If how to send message directly to you rather than posting. I tried alot but do not know how to contact directly.


Thank you,

Kind Regards


Hi Shabana,


Please feel free to email me at I'll be happy to chat more with you about this topic.


I'll look forward to hearing from you then.


Yen Nguyen

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