Sentiment analysis is a process used to determine the attitude of a writer or speaker with regard to a particular topic (positive, negative or neutral). Aspect-based sentiment analysis goes a step further by looking more specifically at people’s attitudes towards different aspects, such as “quality”, “price” or “service”. This allows for a more detailed and nuanced understanding of how people feel about a particular product or service.
In recent years, aspect-based sentiment analysis has gained in popularity due to advancements in technology that have made aspect-based sentiment analysis AI more reliable, accurate and accessible than ever before.
This concise guide will provide an overview of aspect-based sentiment analysis, including what it is, how it differs from other methods, and why it’s important to use up-to-date technologies. Symanto offers a variety of tools that can help you get started with aspect-based sentiment analysis today.
What is Aspect-Based Sentiment Analysis?
Aspect-based sentiment analysis is a method of sentiment analysis that focuses on the identification and classification of opinionated statements regarding specific topics or features. This type of analysis is often used to gain insights into how customers feel about a particular product or service, as it can provide more detailed and nuanced information than other methods of sentiment.
The Evolution of Sentiment Analysis
Document-level sentiment analysis
Early iterations of sentiment analysis classified whether an opinion was positive, negative or neutral on a document level. This means that words in an entire body of text would be labelled as either positive, neutral or negative and then give an overall sentiment.
Sometimes this analysis can be helpful. For example, take this Amazon review for Apple Airpods:
“These are brilliant, the noise cancellation is better than expected with a discreet and comfortable fit. Perfect all-rounder.”
This positive review would be given a positive sentiment score.
But now take a look at this more nuanced review:
“Ok – let’s get it out of the way- they’re expensive, too expensive, even at the ~9% off they briefly hit before Black Friday. But I don’t regret buying them. Signal strength is good, I can wear them and leave my phone in the next room if I want. […] Sound is OK, it could be better for the price really, it’s clear and has a good range, but the low-mid is not as punchy as some models […] Noise cancelling, I think, is excellent.”
This review is positive, and a document-level analysis would concur, but it’s also full of useful information for product development and marketing teams that would be lost if we just analysed for sentiment on a document level.
Concept-level sentiment analysis
To address this issue, aspect-based sentiment analysis was developed. This type of sentiment analysis breaks a document down into smaller concepts and then analyses the sentiment around each aspect.
For example, in the Amazon review above, we can see that there are three main concepts: price, sound quality, and noise cancelling. The aspect-based sentiment analysis would label the sentiment around each of these concepts separately, rather than giving the entire review a single sentiment score.
This type of aspect-based sentiment analysis is often used by businesses to gain insights into how customers feel about different features of their product or service. It can be helpful for product development teams who want to improve their offerings, as well as marketing teams who want to better understand customer sentiment around key topics.
Advancements in aspect-based sentiment analysis technology
Aspect-based sentiment analysis has been around for a while, it has only recently gained in popularity due to advancements in technology. In particular, machine learning algorithms have become more reliable and accurate at identifying and classifying opinionated statements.
It’s important to note that not all algorithms and therefore not all tools available on the market are equally reliable and accurate, and the technology is still advancing.
When choosing a tool for aspect-based sentiment analysis, it’s important to make sure the company is committed to updating its products with up-to-date natural language processing (NLP) technologies and investing in research and development.
At Symanto, we’re spearheading NLP research and are committed to continually applying the latest technologies to our offerings.
Another way that sentiment analysis tools are advancing is through their usability. Historically, many people have felt intimidated by the idea of using NLP algorithms and have found the process of setting up and running sentiment analysis to be complicated.
At Symanto, we’ve made it our mission to make advanced NLP technologies accessible to everyone, regardless of their technical background. Our tools are easy to use, navigate and customise. You don’t need to be a data scientist to get started with aspect-based sentiment analysis.
How Symanto Tools Work
Symanto offers a variety of tools that can be used to gain insights into customer sentiment. Our technology is based on a combination of rule-based and machine learning algorithms. Our team of experts have developed a set of rules that the algorithm uses to identify and classify aspect-based sentiment.
This type of analysis can be performed on a variety of text-based data. For example, through our Symanto Insights Platform (SIP) you can upload xls and csv files with your own CRM data, chatbot transcripts or consumer survey data. SIP is also connected to over 75 review sites, including Amazon, Google, G2Crowd and Gartner. Our experts are on hand to crawl data from social media sites and other channels where opinions are shared.
We’ve created industry-specific models for more precise analysis for certain industries including:
- Banking,
- Car Dealerships,
- Consumer Electronics,
- E-Commerce,
- Hospitality, and
- Pharma.
Get Started with Symanto
We’ve recently updated the Symanto Insights Platform to make it more accessible and user-friendly than ever before.
The Symanto Insights Platform is a great way to explore what our tools and technologies are capable of. Right now, we’re offering a free 21-day trial and 500 free credits if you register today. So now is a great time to find out for yourself how aspect-based sentiment analysis can help your business. Register now or get in touch to find out more.
