What is Aspect-Based Sentiment Analysis

If you were thinking sentiment analysis was neat, you would surely be amazed by an even more progressive text analysis technique: aspect-based sentiment analysis.

This technique is also known as aspect sentiment analysis, feature-based sentiment, or aspect-level sentiment analysis. It enables businesses to analyze their customer data to learn about their emotions towards their products and services. Plus, businesses with this data can improve their products and services to meet their customers’ needs.

In this guide, find out everything about aspect based sentiment analysis, what it can do for your business, and how this works:

About Aspect Based Sentiment Analysis 

A text analysis technique known as aspect-based sentiment analysis (ABSA) classifies data by aspect and finds the sentiment associated with each one. By connecting particular attitudes with various characteristics of a good or service, aspect-based sentiment analysis can be used to analyze consumer feedback.

Aspects are the characteristics or elements of a good or service, such as “the customer satisfaction of a new product,” “the response time for a question or complaint,” or “the ease of integrating new software.”

Here’s a breakdown of what this analysis can extract:

  • Aspects: the feature, topic, or category that is being talked about
  • Sentiments: negative or positive opinions about a particular aspect

Why Is Aspect Based Sentiment Analysis Crucial? 

The ability to automatically organize and analyze client data, automate customer care duties, and instantly acquire insightful information makes aspect sentiment analysis crucial for businesses.

Customers are speaking up more than ever. They like interacting with their favourite brands and giving feedback, both positive and negative. Customers leave a plethora of information every time they connect with a company, be it through a mention or a comment, letting businesses know where they are doing good and poorly.

But manually sorting through all of this data might be challenging. Aspect-based sentiment analysis handles the grunt task for you.

  • Aspect-Based Sentiment Analysis Is Scalable 

It is difficult for teams to manually go through hundreds and thousands of customer reviews, conversations, or tweets- especially when they want to analyze information on a granular level. This is where aspect-based sentiment analysis comes in. It enables businesses to analyze thousands of data in detail automatically. This automation helps businesses to save time and money, and also, they can focus on other important tasks of the organization.

  • Perform Real-Time Analysis 

Aspect based sentiment analysis enables businesses to improve the aspects of products and services that customers are complaining about. Is there a major bug in some software? Is there a glitch in the app? Aspect based sentiment analysis can help you immediately to identify such a situation and take action instantly.

  • Accurate Results

When a human analyzes the meaning behind an aspect or sentiment, they are much likely influenced by their own experience, beliefs, and thoughts. With the help of aspect based sentiment analysis tool, business uses the same essence to find the meaning behind the comments and feedback.

How to do Aspect-Based Sentiment Analysis 

Now you may have understood how important and beneficial sentimental analysis is. So let’s look at how to do it:

Gather data 

Any text analysis must first begin with data collection. But from where does all this information come, and how might corporations collect it? Businesses have gathered enormous volumes of data for years, but they are now beginning to understand their potential.


Conducting aspect-based sentiment analysis on survey data can yield a wealth of information for any business. Online survey generators like Type form and SurveyMonkey make creating and distributing surveys simple, and integrations with text analysis software can automate the procedure.

Open-ended inquiries, for example, you can show what a client feels about various facets of the user experience: “uncomplicated to use and simple user interface (positive), although there are constant issues (negative).”

NPS (Net Promoter Score) 

Many businesses collect and examine customer feedback using NPS software, such as Promoter.io, Delighted and Satismeter. You can automatically sort data using an aspect-based sentiment analysis pattern and learn more about particular facets or qualities of your good or service.

CRM (Customer Relationship Management) Software and customer service 

This is the customer-communication software that organizations employ, such as Zendesk, Help Scout and Freshdesk. Consider all the requests you handle from various sources. They are rife with unstructured data.

With an aspect-based sentiment analysis model, relevant information may be categorized, whether to quickly pinpoint features of a good or service that customers dislike or to point out specific issues.

External Data 

As soon as you go to any social media platform, you will be bombarded with plenty of news articles, product reviews, etc. Now that more and more companies are making their databases public, the amount of external and internal data for optimizing business processes has also turned massive. With the help of text analysis models like aspect-based sentiment, this large data can be easily handled. Furthermore, this tool can easily interpret the whole data quickly and accurately.

But can one find out the relevant data?  

Web Scrapping Tools: 

Web scrapping tools have become quite essential for data collection from external sources.

This tool is sub-divided into two categories:

Web Scrapping Frameworks (for coders): For the coders, several open-source frameworks can be used to make a web scrapping framework.

Visual Web Scraping Tools (for noncoders): Noncoders can use web scrapping tools without using codes.


With the help of API, one can easily let two applications communicate with each other. Due to this, it becomes easy to extract useful data from social media channels and other platforms. Companies like Facebook, Instagram, and Twitter have their APIs, which allow data extraction quite specific and easy.

Final Words 

Aspect Based Sentiment Analysis is essential when understanding the customer’s feelings or point customer. In addition, it helps analyze the feedback a business gets from its customers. With the help of this, they can make smart choices for improving the quality of products and services.

If you are looking forward to starting with the Aspect Based Sentiment Analysis, VizRefra is here to aid you. We will provide you with a smart aspect-based sentiment analysis model to help your business grow.