Product

Aspect-Based Sentiment Analysis now available in AYLIEN Google Sheets Add-on

Introduction

As you may be aware, we recently boosted our Text Analysis API offering with a cool new feature, Aspect-Based Sentiment Analysis. The whole idea behind Aspect-Based Sentiment Analysis (ABSA) is to provide a way for our users to extract specific aspects from a piece of text and determine the sentiment towards each aspect individually. Our customers use it to analyze reviews, Facebook comments, tweets and customer feedback forms to determine not just the sentiment of the overall text but what, in particular, the author likes or dislikes from that text

We’ve built models for 4 different domains (industries). You can see the domains and the domain specific aspects listed in the image below.

 

 

Not familiar with Sentiment Analysis? We explain it quickly and simply here to help get you up to speed. You can also try it for yourself with our easy-to-use online Demo.

ABSA for the AYLIEN Google Sheets Add-on

We’ve just added the ABSA features to our Google Sheets Add-on. The add-on enables users to perform Text Analysis functions from within Google Sheets, without any coding/programming knowledge. We designed the add-on to be as simple and user friendly as possible. If you are in any way familiar with Google Sheets (or Microsoft Excel), you’ll be up and running in no-time.

It’s so simple, in fact, that we can analyze the sentiment of text (hotel reviews in this instance) in under 15 seconds.

Analyzing hotel reviews from Yelp

To showcase our latest feature addition, we analyzed 500 hotel reviews, from Yelp, within Google Sheets. We’ve embedded the sheet we used below to show you exactly how the add-on functions. 

Findings

After running Aspect-Based Sentiment Analysis on the 500 hotels reviews, we had an abundance of data and information to draw insights from. As you can see from the “Analyzed Reviews” sheet in the embedded spreadsheet below, the results of our analysis are laid out neatly in columns containing the review text, the review rating (out of 5) and the positive, negative and neutral aspects found within each review.

Looking beyond the overall sentiment of a review tells us what exactly the customer liked or disliked about their experience. It allows a much greater understanding for the voice of the customer.

Aspects Mentioned

The first thing we wanted to figure out was what aspects the reviewers were speaking about across all reviews and how often each aspect was mentioned.

 

 

It’s interesting how little WiFi is mentioned in reviews when it can, in our opinion, be one of the more frustrating aspects of a hotel’s service. It’s also pretty clear from the graph that people are most opinionated about a hotel’s location and the amenities of their room (TV, Iron, mini bar etc…).

World Cloud

We used another simple Sheets Add-on, Word Cloud Generator, to produce word clouds from the most cited positive and negative aspects. Straight away we can see that the two biggest complaints among customers are the room itself and hotel amenities.

 

Sentiment per Aspect

 Having understood what it is people talk about in reviews we also wanted to dive into the opinion towards each aspect across all the reviews.

To do this we generated aspect-specific pie charts to generate more targeted visualizations (You can see interactive versions in the “Graphs” sheet above).

Note: It’s important to keep in mind the percentages shown are based on the reviews that mention that particular aspect. So taking WiFi, for example, 45.9% of reviews that mentioned this aspect were negative.

 

 

Analyzing reviews on a per aspect basis enables hotel management, for example, to focus on and track the performance of specific aspects, departments or services, such as food & beverage or how the staff are perceived by guests. It can also help to uncover correlations between certain aspects – does an increase in positive sentiment toward Staff lead to an increase in positive sentiment towards Value?

Sentiment of Aspects

The last thing we want to do was to try and get a high level overview of the entire collection of reviews. The graph we created below gives a really nice overview and comparison of customer sentiment towards each of the various aspects across all reviews. It gives a snapshot view of hotel performance as a whole according to all of the reviews.

Conclusion

We’re really excited about this latest addition to our Text Analysis API and Google Sheets Add-on. The analysis we performed above really was so simple that we believe anyone can do the same with their own text/data.

Wanna try it for yourself? You can access the AYLIEN Google Sheets Add-on for free and we’ll even give you 1,000 credits free to run your own analysis.

If you want to explore ABSA a bit deeper (and 12 other Text Analysis features) you can sign-up for our Text Analysis API which will enable you to make up to 1,000 API calls per day. No credit card required. Click the image below to get started!

 




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Author


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Mike Waldron

Head of Marketing & Sales @ AYLIEN A legal convert with a masters degree from Smurfit Business School, Mike runs our Sales and Marketing at AYLIEN. Mike gathered his Sales and Marketing experience with technology companies in Sydney and Dublin before getting the startup itch and joining the team at AYLIEN. Twitter: @MikeWallly