Why user reviews visualization needs emotion: a proof of concept.

In the last decade many popular web and mobile services have collected huge (and precious) amounts of data about users’ opinions, which are generally expressed in form of ratings and reviews. However, the way most of these services present the data collected to their end-users is often old-fashioned and mainly consists of simple numbers and plain charts.

For instance, let’s take TripAdvisor as a test case. TripAdvisor is one of the main references worldwide for hotels and restaurants reviews, which can drive the choices of millions of travelers when visiting a new location, hence leading to a significant real-world economic impact for local businesses and associated services.

Nonetheless, the way content is presented by TripAdvisor can sometimes be confusing to the general audience. Despite having collected a considerably rich amount of data, in fact, these information are displayed using tens of numeric values associated to basic plots such as horizontal bar charts:


These visualization choices are not as immediate as they could be, and require further interpretation by the user (e.g. having 54 terrible reviews on a total of 1200 is different than having 54 terrible reviews on a total of 100, and this is not immediately reflected in the information displayed).

In the era of new data visualization techniques and infographics, something better can be done. This is precisely the motivation behind the creation of MoodAdvisor, a free web & mobile app that allows travellers to quickly visualize in a very graphical and straightforward fashion the excellency of a hotel and the the happiness of previous travelers who left a review. The former measure is based on the number of excellent and very good reviews normalized according to the total number of reviews and converted into a percentage, while the latter measure is based on state-of-the-art algorithms of sentiment analysis to interpret the affect in the reviews left on TripAdvisor for a certain location.

MoodAdvisor allows the user to search for any hotel worldwide through a simple form, then retrieves the data from TripAdvisor, computes in real-time the aforementioned values of excellency and happiness, and visualizes the results using gauges followed by colored boxes representing the latest reviews associated to simple emotions (happy, neutral, sad):


For the front end, MoodAdvisor makes use of jQuery UI + Javascript to asynchronously retrieve data and of HTML5 + Google charts to visualize the results. The back-end (data scraping and sentiment analysis) is developed using PHP.

Unfortunately, when requesting to access the official API, TripAdvisor did not approve the use MoodAdvisor did of their data since it was in violation of their Terms of Use and I was explicitly requested not to go live with my service. For this reason, MoodAdvisor was never officially launched :( However, the technology behind MoodAdvisor is fully functional and can be applied to many other web services and datasets (e.g. reviews of products on Amazon etc…). Moreover it constitutes a proof of concept on how the visualization of user ratings and reviews not only could be graphically more informative, but also should introduce novel measures, such as emotion, which are intrinsic to human communication capabilities and therefore convey information in a more efficient and straightforward way.

MoodAvisor can still be tried at moodadvisor.com, even though the results presented now are based on dummy data. I also released part of the code (data scraping from a webpage using regular expressions) on GitHub.

This post originally appeared on LinkedIN.

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