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Aspect Based Sentiment Analysis
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<h1>Aspect Based Sentiment Analysis</h1>
<p>Aspect-based sentiment analysis is a <a href="https://monkeylearn.com/text-analysis/" target="_blank"
rel="noopener noreferrer">text analysis</a> 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).</p>
<p>If you thought <a href="https://monkeylearn.com/sentiment-analysis/" target="_blank"
rel="noopener noreferrer">sentiment analysis</a> 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. </p>
<p>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.</p>
<p>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?</p>
<p>Cue aspect-based sentiment analysis (ABSA). This technique can help businesses become <a
href="https://www.salesforce.com/blog/2019/01/how-to-create-a-customer-centric-experience.html"
target="_blank" rel="noopener noreferrer">customer-centric</a> 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. </p>
<p>But how can you get started with aspect-based sentiment analysis?</p>
<p>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.</p>
<p>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!</p>
<p>If there’s something in particular you want to know about aspect-based sentiment analysis, click on the content
links below and we’ll take you straight there:</p>
<ol>
<li><a href="/#what-is">What is Aspect-Based Sentiment Analysis?</a> </li>
<li><a href="/#importance">Why is it Important?</a></li>
<li><a href="/#use-cases">Use Cases & Applications</a></li>
</ol>
<p>It’s time to learn more about aspect-based sentiment analysis. Let's get started!</p>
<h2 class="section" id="what-is">1. What is Aspect-Based Sentiment Analysis?</h2>
<p>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. </p>
<p>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. </p>
<p>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'. </p>
<p>Here’s a breakdown of what aspect-based sentiment analysis can extract:</p>
<ul>
<li><strong>Sentiments:</strong> positive or negative opinions about a particular aspect.</li>
<li><strong>Aspects:</strong> the thing or topic that is being talked about. </li>
</ul>
<h2 class="section" id="importance">2. Why is Aspect-Based Sentiment Analysis Important?</h2>
<p>Customers are more vocal than ever. They love leaving feedback – good and bad – making them a valuable resource
for businesses. <a href="https://www.lyfemarketing.com/blog/social-media-marketing-statistics/" target="_blank"
rel="noopener noreferrer">A huge 95% of adults between the ages of 18 and 34 are likely to follow a brand on
social media</a>, and each time they interact with the brand, whether it’s a mention or comment, brands are
receiving valuable insights. </p>
<p>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 <a
href="https://www.lithium.com/company/news-room/press-releases/2013/consumers-will-punish-brands-that-fail-to-respond-on-twitter-quickly"
target="_blank" rel="noopener noreferrer">72% of customers who complain to a brand on Twitter expect a
response within an hour</a>?</p>
<p>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 <a
href="https://www.superoffice.com/blog/what-customers-want-you-to-know-about-them/" target="_blank"
rel="noopener noreferrer">customers are willing to spend more money with a company that delivers an excellent
service</a>, which goes to show that people are less price conscious these days, and more focused on a premium
customer experience.</p>
<p>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.</p>
<p>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 <a href="https://hbr.org/2014/10/the-value-of-keeping-the-right-customers" target="_blank"
rel="noopener noreferrer">create a seamless customer experience and increase customer retention</a>. </p>
<p>Let’s take a look at some of the advantages in more detail: </p>
<h3>Scalability</h3>
<p>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.</p>
<h3>Real-Time Analysis</h3>
<p>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. </p>
<h3>Consistent Criteria</h3>
<p>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 <a
href="http://ceur-ws.org/Vol-1096/paper1.pdf" target="_blank" rel="noopener noreferrer">agree around 60-65% of
the times</a> 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.</p>
<h3>Deeper customer understanding</h3>
<p>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. </p>
<h2 class="section" id="use-cases">3. Use Cases and Applications</h2>
<p>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. </p>
<p>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. </p>
<h3>Product Feedback</h3>
<p>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 <a
href="https://community.uservoice.com/blog/5-overlooked-customer-feedback-opportunities-that-are-product-insight-goldmines/"
target="_blank" rel="noopener noreferrer">key to discovering and solving customer problems</a>. </p>
<p>Here’s how aspect-based sentiment analysis can be used to make sense of all this customer feedback:</p>
<ul>
<li>Understand specific aspects that customers like and dislike about your brand.</li>
<li>Get valuable, granular-level insights from customer feedback.</li>
<li>Analyze service and product reviews to discover the successes and failures of your brand, and compare them
to your competitor’s.</li>
<li>Track how customer sentiment changes toward specific features and attributes of a service or product.</li>
<li>Determine if customer segments feel more strongly about a specific feature, for example an older demographic
might find a travel website harder to navigate than a younger demographic.</li>
</ul>
<h3>Customer Support</h3>
<p><a href="https://www.teamsupport.com/blog/customer-support-metrics-to-track" target="_blank"
rel="noopener noreferrer">Customers don’t like waiting for a solution to their problems</a>, 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: </p>
<ul>
<li>Automate tagging of all incoming customer support queries.</li>
<li>Quickly find out why customers are unhappy.</li>
<li>Send queries and complaints to team members that are best equipped to respond.</li>
<li>Gain insights into how your customer support team handles customers.</li>
</ul>
<h2>Final Words</h2>
<p>The customer experience is top priority among businesses. It will rise to the <a
href="https://www.forbes.com/sites/shephyken/2018/12/16/ten-customer-servicecustomer-experience-predictions-for-2019/#42a1eaef6178"
target="_blank" rel="noopener noreferrer">top of the marketing agenda</a> and continue to be one of the best
investments a company can make. For consumers, the <a
href="https://www.qminder.com/customer-service-statistics/" target="_blank" rel="noopener noreferrer">customer
experience will become more important than price and product</a> by 2020. </p>
<p>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. </p>
<p>For example, a software company might want to understand the specific sentiments towards different aspects of
its product. A review might say: <em>"support were great but UI is confusing”</em>, 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.</p>
<p>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. </p>
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