Aspect-based sentiment analysis is a text analysis technique that breaks down text into aspects (attributes or components of a product or service), and then allocates each one a sentiment level (positive, negative or neutral).
If you thought sentiment analysis was pretty neat, then prepare to be blown away by this advanced text analysis technique, aspect-based sentiment analysis helps you get the most out of your data.
Imagine you have a large dataset of customer feedback from different sources such as NPS, customer satisfaction surveys, social media, and online reviews. Some positive, some negative and others that contain mixed feelings. You’d use sentiment analysis to automatically classify the polarity of each text, right? After all, it’s already proven to be a highly efficient tool.
But, what if you wanted to pick customer feedback apart, hone in on the details, get down to the nitty-gritty of each feedback for a more complete picture of your customers’ opinions?
Cue aspect-based sentiment analysis (ABSA). This technique can help businesses become customer-centric and place their customers at the heart of everything they do. It’s about listening to their customers, understanding their voice, analyzing their feedback and learning more about customer experiences, as well as their expectations for products or services.
But how can you get started with aspect-based sentiment analysis?
First, you’ll need to gather data, such as customer feedback, reviews, survey responses, social media and more. Next, you’ll need to analyze the information using aspect-based sentiment analysis. There are often hundreds or thousands of text entries from each source, so it would be far too time-consuming and repetitive to analyze them manually. And if you want to analyze information on a granular level, in the same way an aspect-based sentiment analysis does, it would be near impossible without machine learning.
Below, we’ll go into more detail about what aspect-based sentiment analysis is, how it works, and how you can use it within your business. Then you’ll be able to create your own aspect-based sentiment analysis model – yes, even if you’ve never written a line of code!
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The big difference between sentiment analysis and aspect-based sentiment analysis is that the former only detects the sentiment of an overall text, while the latter analyzes each text to identify various aspects and determine the corresponding sentiment for each one.
In other words, instead of classifying the overall sentiment of a text into positive or negative, aspect-based analysis allows us to associate specific sentiments with different aspects of a product or service. The results are more detailed, interesting and accurate because aspect-based analysis looks more closely at the information behind a text. Scientists, for example, analyze cells under a microscope so that they can better visualize their components, and aspect-based sentiment analysis follows this principle.
When we talk about aspects, we mean the attributes or components of a product or service e.g. 'the user experience of a new product', 'the response time for a query or complaint' or 'the ease of integration of new software'.
Here’s a breakdown of what aspect-based sentiment analysis can extract:
Customers are more vocal than ever. They love leaving feedback – good and bad – making them a valuable resource for businesses. A huge 95% of adults between the ages of 18 and 34 are likely to follow a brand on social media, and each time they interact with the brand, whether it’s a mention or comment, brands are receiving valuable insights.
Now, imagine all that data customer support, product and analytics teams have to deal with, and we’ve only touched upon the one data source (social media)! We’ll go into more detail about the different data sources that exist later on. There’s also the added pressure of dealing with customer demands as quickly as possible. Did you know that 72% of customers who complain to a brand on Twitter expect a response within an hour?
It all boils down to customer experience. If customers are unhappy with the way you handle queries and complaints, or finds the user experience of your software clunky and inefficient, they’ll simply look elsewhere for an alternative. American Express found that customers are willing to spend more money with a company that delivers an excellent service, which goes to show that people are less price conscious these days, and more focused on a premium customer experience.
Aspect-based sentiment analysis works in the same way as sentiment analysis. It takes all that data – emails, chats, customer surveys, social media posts, customer support tickets etc – and automatically structures it so that companies are able to interpret text entries from customers and gain meaningful insights. Not only does this help managers make key decisions based on insights from their customers, it also helps employees become more efficient and less frustrated with time-consuming, monotonous tasks.
Aspect-based sentiment analysis is particularly relevant at the moment because companies need to be more customer-centric than ever. This text analysis model lets businesses read between the lines, and hone in on the specific aspects that make their customers happy or unhappy. By gaining a deeper understanding, businesses are then able to create a seamless customer experience and increase customer retention.
Let’s take a look at some of the advantages in more detail:
It’s impossible for teams to manually sift through thousands of tweets, customer support conversations, or customer reviews, especially if you want to analyze information on a granular level. Aspect-based sentiment analysis allows businesses to automatically analyze large amounts of data in detail, which saves money, time and means teams can focus on more important tasks.
Aspect-based sentiment analysis allows businesses to hone in on aspects of a product or service that customers are complaining about, and make amends in real-time. Is there a glitch in an app? Is there a major bug in some new software? Are customers getting angry about one particular service or product feature? Aspect-based sentiment analysis can help you immediately identify these kinds of situations and take action.
While we’re able to differentiate between different aspects and sentiments within a text, we’re not always objective. We’re influenced by our personal experiences, thoughts, and beliefs and only agree around 60-65% of the times when determining sentiments for pieces of text. By using a centralized aspect-based sentiment analysis model, businesses can apply the same criteria to all texts meaning results will be more consistent and accurate.
It’s easier to scan and categorize text as positive or negative than it is to spend time analyzing each sentence of a text. But by using an automated aspect-based sentiment analysis system, companies can gain a deeper understanding about specific products and services quickly and easily, and really focus on their customers’ needs and expectations. It means businesses take into account everything a customer says and can create a customer-centric experience.
Aspect-based sentiment analysis is a very versatile text analysis model that can be used across all industries and departments, to automate business processes and gain more accurate insights to make better decisions.
In this section, we’re going to focus on how aspect-based sentiment analysis is being used to analyze customer feedback (VoC) and improve customer service.
Today, there’s an abundance of feedback on social media, your Net Promoter Score (NPS), websites and much more, and all this textual customer feedback is key to discovering and solving customer problems.
Here’s how aspect-based sentiment analysis can be used to make sense of all this customer feedback:
Customers don’t like waiting for a solution to their problems, which means customer support teams need to respond quickly and effectively. If not, chances are customers will look elsewhere. That’s why businesses need high-quality machine learning software like aspect-based sentiment analysis to:
The customer experience is top priority among businesses. It will rise to the top of the marketing agenda and continue to be one of the best investments a company can make. For consumers, the customer experience will become more important than price and product by 2020.
Machine learning is at the forefront of this movement. It can help businesses become customer-centric by listening to them, understanding their voice and analyzing their feedback. Sentiment analysis is already being used to automate processes, but it only determines polarities of a text – negative/positive, good/bad, beautiful/ugly. Aspect-based sentiment analysis, on the other hand, is able to gain a much deeper understanding of textual data.
For example, a software company might want to understand the specific sentiments towards different aspects of its product. A review might say: "support were great but UI is confusing”, which contains a positive sentiment towards 'aspect customer support' but a negative sentiment towards 'aspect user experience'. A sentiment analysis model might classify the overall sentiment as negative, and ignore the fact that the staff did a good job, or vice versa. Whereas an aspect-based analysis model would differentiate between aspects and allocate a sentiment to each one.
Once data has been imported, either from internal or external sources, aspect-based analysis tools are able to classify sentiments towards specific product features or services. And this is where it gets interesting for organizations. Customers want to feel like they’re being listened to, and by using deeper machine learning models like aspect-based sentiment analysis, businesses can send quick, efficient and personalized responses. And for customer support teams it means streamlining processes and gaining more valuable insights.