Feature Bundle – Text Analysis for Customer Support

Feature Bundle – Text Analysis for Customer Support


In a world that is increasingly focused on SLAs, KPIs and ROIs, the role of Customer Support and Customer Success, particularly in SaaS companies, has never been more important to the overall performance of an organization.

These ‘departments’ are no longer siloed entities within organizations and a support team is no longer a nice-to-have addition to a product or service offering. Rather, organizations are now focused on building customer success-centric processes throughout, as the shift from nice-to-have to need-to-have becomes apparent. A customer success-centric company focuses on every detail of, and interaction with, their customers to ultimately increase brand loyalty and reduce churn.

How can Text Analysis help?

Using Machine Learning and Natural Language Processing techniques, it’s easier than ever to understand and analyze all your customer interactions, whether it is direct via email, feedback forms, NPS surveys, live chat or mentions on social media channels.

To make things easy for you, we’ve put together a list of Text Analysis features (or endpoints as we call them) that are being widely used by our customers to boost their support offerings. So if you’re new to using our API this will help you get up to speed quickly;

  1. Semantic Labelling
  2. Entity & Concept Extraction
  3. Sentiment Analysis

Note: We have a really cool and easy-to-use Demo showcasing all of these features. You can test them out with your own text data (articles, tweets URLs etc) or try some of our sample queries.

Let’s begin by taking a look at the first feature on our list, Semantic Labelling.

1. Semantic Labelling

Because of the multi-channel nature of customer support, you tend to have customer queries and requests coming in from a variety of sources – email, social media, feedback forms, live chat. Speed of response is a key performance metric for many organizations and so routing customer queries to the relevant department, in as few steps as possible, can be crucial.

An obvious solution is to employ a gatekeeper of sorts, who manually alerts each individual department when a customer request is received from one of the aforementioned channels. But it’s 2016, and we believe the role of human-switchboard should be a defunct one!

Wouldn’t it be easier if customer queries were automatically routed to the correct department or individual, without any human interaction?

Of course it would! Semantic Labelling selects which label best represents a piece of text based on semantic similarity. By providing specific labels to the various departments within your organization, support queries can be analyzed, tagged and routed accordingly.

Here’s a quick example. A user of one of your products is unable to find documentation on your website. They open up your feedback form and ask the question;

You have 3 main departments within your organization that respond to customer queries; Support, Marketing and Sales. The Semantic Labelling feature will automatically analyze this question and order your labels (departments) accordingly;

In this case, Sales or Marketing do not need to be notified of such a request, so it can be automatically routed to the Support team.


By getting customer queries to the right person within your organization ASAP, you significantly reduce the risk of it being lost or sent to the wrong department or individual. The main benefit here is streamlining and automating the support process to ensure your customer gets a response in the shortest time possible.

2. Entity & Concept Extraction

Customer support request content can contain a wealth of mentioned entities and values that can provide some really interesting and important information. The challenge is mining this information, particularly at scale. To best understand this content, you want to know the who, the what and the how much from each and every customer query or request.

Entity Extraction extracts named entities (people, organizations, products and locations) and values (URLs, emails, telephone numbers, currency amounts and percentages) mentioned in any body of text.

Concept Extraction extracts named entities mentioned in your support requests and cross-links them to DBpedia and Linked Data entities, for greater understanding.

Concept extraction also disambiguates similarly named entities. Take Apple for example. If it is mentioned is it a reference to the company or the fruit? Concept extraction analyzes the content around the word and through Machine Learning and Natural Language Processing (NLP) techniques, performs the disambiguation.


By extracting entities and concepts from your support requests and analyzing at scale you can gain some incredibly useful insights and trends and get answers to key questions, such as;

  • What words, phrases, brands, products are mentioned most?
  • Has there been a spike in mentions of a particular product or service?
  • Are they mentioning or comparing us to other brands?
  • Are they mentioning specific people or places in relation to our brand or product? Perhaps our service is failing in certain areas.

3. Sentiment Analysis

At the heart of our customer support efforts is the happiness of our customers. Naturally, we want them to love our product or service, and to be cared for when something goes wrong or doesn’t quite work as intended. This, of course, is the overall goal and it is one that will affect all areas of your organization as it directly impacts brand loyalty, customer retention and churn levels. But how exactly do we measure happiness and understand how our customers feel about us, our products, our brands etc? Sentiment Analysis.

Sentiment Analysis detects the sentiment of a body of text in terms of polarity (positive or negative) and subjectivity (subjective or objective). When used, particularly at scale, it can show you how people feel towards the topics that are important to you – particularly your brand and product offerings.

Whether you are analyzing feedback forms, chat transcripts, emails or social media, Sentiment Analysis will help you hear the true voice of the customer and how they feel about specific topics or areas of interest to you.


Analyzing the sentiment towards your brand can help you decrease churn and improve customer support by uncovering and proactively working on improving negative trends. It can help show you what you are doing wrong before too much damage has been done, but also quickly show you what you are doing right and should therefore continue doing.

Customer feedback containing significantly high levels of negative sentiment can be relayed to Product and Dev teams to help them focus their time and efforts more accordingly.

Aspect-Based Sentiment Analysis

While Sentiment Analysis provides fantastic insights, the overall sentiment of customer feedback or comments on social media won’t always pinpoint the root cause of the author’s frustrations, or praise.

This is where Aspect-Based Sentiment Analysis (ABSA) comes in. With ABSA, you can dive deeper and analyze the sentiment toward industry-specific aspects.

Customer requests, feedback forms, NPS surveys and social media posts, such as tweets, facebook posts and reviews, may contain fine-grained sentiment about different aspects (e.g. a product or service) that are mentioned in the content. For instance, a review about an airline may contain opinionated sentences about its staff, food, punctuality and value. This information can be highly valuable for understanding customers’ opinion about a particular service or product.

Let’s put this example to the test. We grabbed an airline review and ran it through our ABSA endpoint. Here’s the review:

As a backpacking student Ryanair was really my only option (being the cheapest!). I think their bad reputation is built mostly on people who simply don’t read the terms of their flight. It’s a cheap A-B service that saves you a tonne of cash if you stick to their rules. I actually found the staff to be very friendly and helpful. No complaints there! What I will complain about is the food on offer. I understand plane food is bad in general but this is another level of bad. The food is disgusting a overpriced unfortunately. Also, the seats are a uncomfortable for people like me who are over 6 feet tall. It was difficult to relax at times. My flight arrived on time which was great. This meant I made my connecting train. Happy days! Overall I was very pleased with Ryanair. The price I paid was really cheap in comparison to the others on offer and as someone looking to save money, this was really all I was after – a cheap flight.

And here are the results;

As you can see from the results above, the ABSA endpoint automatically pulls airline industry-specific aspects (such as food, staff, punctuality, comfort and value), performs Sentiment Analysis on each aspect and gives sample sentences to indicate examples of where the the score was derived.


ABSA helps you locate aspect-specific issues and proactively resolve them before they snowball into a more serious issue. It enables you to pinpoint failings in support offerings or product/service communications by monitoring the levels of customer sentiment and generating trend reports.

ABSA is currently available for four industries – Airlines, Hotels, Cars and Restaurants – and we are already working on expanding this list. Here are the aspects covered for our initial four industries;


We hope this post gives you some inspiration and helps you to understand the various Text Analysis features available to you and how they can help you with your customer support efforts.

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