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Using An Opentable API For CX Sentiment Analysis

An OpenTable API gives restaurants much needed competitive advantage due to its ability to extract sentiment and deep insights from customer reviews. In the US alone, statistics confirm that more than 90% of diners read reviews before going to a local restaurant, and 31% say they are willing to spend more money if the business has positive reviews.

This means that as a marketer in the hospitality industry you can leverage this massive data for sentiment analysis on reviews to not only discover guest insights and hospitality trends but also develop better strategies for more satisfying dining experiences.

Why Do Businesses Use OpenTable?

Quite the new kid on the block, OpenTable was established in 1998 and is ranked #5 in the restaurants and delivery app segment. Its popularity stems from the fact that it’s a clever app that serves both businesses from a customer enablement perspective and empowers the consumer to get the best dining experiences plus perks.

You can make real-time online reservations for fine dining through the app, read restaurant reviews, earn loyalty points and do a lot more. This includes:

  • Get the latest news on the best dining locations;
  • Earn a VIP status and get 10% off on hotels and restaurants;
  • Message restaurants directly for dietary preferences and special requests;
  • Invite guests directly through the app;
  • Inspect the menu;
  • And ofcourse, book, change, or cancel reservations as well as keep track of past reservations.

While guests get the most out of their fine dining preferences, businesses are able to leverage an OpenTable API for customer experience analysis. It’s a win-win for both.

What Are The Benefits Of Using An OpenTable API For Analyzing Sentiment?

An OpenTable API empowers you to fortify yourself against the highly competitive world of fine-dining, a place where you have competitors at every corner. The insights you get from an OpenTable sentiment analysis solution, allow you to monitor and track emerging industry trends, improve brand experience, address customer likes and dislikes, and more.

Below we list ten of the most important benefits you can expect from an OpenTable API

  1. Analyze guest intent and preferences
  2. Discover data patterns to forecast revenue
  3. Comparative analysis with competitors
  4. Get marketing and advertising insights
  5. Strategize brand nurturing to improve visibility
  6. Analyze not only reviews but in-app surveys
  7. Improve guest experience more effectively
  8. Plan employee shifts and optimize them
  9. Multilingual insights
  10. Get aspect-based insights to improve operations

Learn more about customer review analysis.

How Is Sentiment Analysis Done Using An OpenTable API?

Deep learning algorithms summarize, classify, and analyze reviews and extract fine-grained insights from them that are of immense value to a hospitality business. Let’s get a better understanding from a layman’s perspective of how machine learning achieves this feat.

The OpenTable sentiment analysis machine learning model is first trained on OpenTable datasets such as this. Once the model is trained, the results are checked for accuracy regarding aspects, topics, themes, and such against a validation dataset. This process is repeated a few more times, usually two or three, until the model gives the most optimal results. Relatedly, Repustate’s OpenTable API provides an accuracy of 80% or more.

There are four main stages in which the model extracts and provides you with aspect-based customer experience analysis insights.

Stage 1 - Data gathering/uploading

Now that the model is ready to be used, you can upload the reviews directly onto the dashboard via the URL or download the comments onto an excel file and upload them manually. You can also get real-time OpenTable sentiment analysis, for which the platform will conduct repeat, time-bound data pulls automatically, based on how you set them.

Stage 2 - Data processing

Natural Language Processing (NLP) algorithms analyze reviews and process them using native speech taggers for each language the model discovers. This is often the case in cosmopolitan cities where customers often input reviews in their preferred languages such as Spanish, Italian, or even Arabic or Chinese.

All the text from the comments is categorized into topics and themes, entities and aspects such as “food”, “drinks”, “price”, and all emojis added to the text analytics pipeline. This feature allows the algorithms to analyze comments and reviews even from video channels, for example, YouTube comments analysis.

Stage 3 - OpenTable sentiment analysis

All the data that has been cleaned and readied in the aforementioned stage is now processed to derive sentiment from text. All the elements, i.e. topics, aspects, themes, etc are isolated, analyzed for sentiment, and assigned a score between -1 to +1. Eventually, all these scores are calculated to provide an overall brand sentiment score, which is in a percentage ranging from 0, being the least, to 100 as the maximum positive score.

Stage 4 - Visualizing the output

All the insights are shown on a dashboard in the form of user-friendly charts and graphs. You can set alerts through the dashboard for keywords or brand mentions, and also share this data with relevant departments.

Learn more about sentiment analysis steps.

How Do You Choose The Best Tool For Opentable Sentiment Analysis?

Your OpenTable API solution must have an aspect model that is based on the hospitality industry to ensure that topics and aspects are categorized properly. This is a necessity for high-precision guest experience insights.

Apart from this, there are other essential elements that you need to consider in order to choose the best OpenTable API. These are as follow.

1. High-precision NLP capability

Your sentiment analysis API must have the most high-precision NLP capability possible because that’s what plays a major role in giving you accurate insights. Natural language algorithms must make sure that they are reading all text including emojis. Emojis are very common in social media lingo as well as online comments and reviews. NLP algorithms must have the capability to recognize the language of emojis and not ignore them as they are not text.

Similarly, the more comprehensive the NLP capability is, the more the platform will be able to recognize the colloquial language, idioms, and phrases. It should also be able to decode code-switching and social media abbreviations and lingo that is most commonly used while filling out reviews on mobile or other digital devices. Learn how NLP helps analyze sentiment in social media feeds.

2. Aspect-based sentiment analysis

Aspect recognition and sentiment analysis that are based on your data are what will tell you exactly what you can do to improve your guest experience. Without aspect-based emotion mining, you will only get a general overview of customer sentiment about your establishment. This is what most sentiment analysis tools offer apart from the metrics that OpenTable itself provides.

With aspect-based sentiment analysis, you will know why you are not able to meet your benchmark reservations, what you can do to address it and figure out how to plan your branding and promotion campaigns. As a marketer, this is a very important element you need.

3. Multilingual capability

Many sentiment analysis APIs offer multilingual data analysis but what you need to find out is if they are using machine translations. If so, you won’t get the quality insights you need because machine translations leave out a lot of vital information. This is simply because machine translations are not there yet as far as accurate translations are concerned.

For highly accurate OpenTable sentiment analysis, the model must have native language part-of-speech taggers for each language. These speech taggers are painstakingly developed manually by data scientists. If you are in a homogenous location, you may not need a multilingual solution. But if you are located in cosmopolitan geography or have locations spread across countries, you definitely need native multilingual capabilities in your OpenTable API.

4. Entity recognition and extraction

Entity recognition and extraction helps in two ways - it allows you to get competitor insights and it ensures that even if guests misspell restaurants, locations, people, or any other important names, you will still get accurate insights.

Deep learning algorithms for entity extraction know that if the customer writes “John-Georges”, “Jean George”, or “Jean-Giorge”, in reference to fine dining, they actually mean Jean-Georges, the Michelin star, haute cuisine French restaurant. Similarly, if they mention the Ritz but misspell Piccadilly, the model would take that into account too.

5. Sentiment trend

You can see patterns and trends emerging in the data based on different time periods. You can compare historical data with the current and use the insights to decipher what the cause of those patterns is. This could be related to several things such as new restaurant locations, an increase in traffic during certain seasons, or simply events that are out of your control like the covid pandemic that brought business to a standstill.

Having these insights on hand allows you to make sure that you do what is needed to maintain positive trends and make changes where necessary for improved efficiency.

6. Granular data categorization

Granular data categorization will tell you sentiment according to languages, customer demographics, data sources (reviews, in-app surveys, etc), and much more. This way you are fully aware of how guests of different backgrounds experience your restaurants and brand, and if there is a difference in their personal experiences.

7. Speed and scalability

Speed and scalability are important elements where reviews and comments are concerned. As a marketing manager or a data analyst, you need to have insights seamlessly and effortlessly available when you need them. This is also important for real-time sentiment analysis because otherwise, it doesn’t make much sense.

Similarly, scalability is an important factor. A machine learning-enabled OpenTable API is a great investment because it keeps learning from data and thus gives you more and more accurate insights as time passes. But if you scale and your model does not, it doesn’t help at all. The model you choose should be able to grow with you, which means it must be infinitely scalable.

8. Customizable & Easy

Just as the model’s ability to scale, it should also allow you to customize it according to your changing needs. As you improve your restaurant operations, better your menu, add new locations, and make general changes in your business strategy, so does the world around you change.

The OpenTable sentiment analysis platform you choose should be able to adapt to all these changes in your environment. It should allow you to add new metadata, change aspects, set alerts to track negative mentions or a spike in mentions, and many other things. And ofcourse, all this without you needing to code or turn to a third-party vendor.


The best of fine dining restaurants have one thing in common - they strive to give their patrons not only a highly distinguished cuisine but also an experience that is memorable. Based on what kind of restaurant you are and who your target audience is, you can get guest experience analysis from a variety of sources. You can gain insights from TikTok about a demographic that is on the younger side of life. Or you could get customer insights by doing social listening on Instagram. Whatever the channel you use apart from OpenTable, your sentiment analysis solution will give you the insights you need as accurate as they can be if it fulfills the criteria listed above.

Repustate has been collaborating with clients in the hospitality industry the world over for many years. The Repustate OpenTable sentiment analysis solution offers the most precise insights compared to many major players in the market. It extracts insights from 23 languages seamlessly and is available as an API as well as a full-fledged platform with an intuitive dashboard.

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