‘Customer sentiment’ is the meter you want to gauge to nurture enriched customer experiences. Considering the vast amount of feedback organizations receive every day, it can be overwhelming to transform it into actionable insights.

Giving attention to feedback is far more than just finding ways to improve your product, it is also about giving your customers a sense of responsibility in the success of your product making customer feedback a two-way communication channel rather than a one-way ticket to serve the customer.

Challenges in managing customer feedbacks

One of the main challenges with analysing customer feedback is the large amount of data generated, often in the form of natural language. The real challenge is to automatically parse and organise this data into actionable insights. As you set to analyse customer reviews, you can face stumbling blocks in the likes of little or irrelevant information.

Sentiment analysis can get you so far

Sentiment analysis provides only an overall picture of sentiments around a brand. It is not equipped to tell you where your brand stands out or what you must work on. The traditional sentiment analysis extracts only a generalized positive or negative sentiment from each review, but you don’t have the insights to pinpoint a specific feature associated with your product or service that has triggered positive or negative sentiments. This is where Aspect-Based Sentiment Analysis has the edge.

Leveraging Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis help identify aspects and customer sentiments associated with specific aspects of your products or services, and augmenting these aspects to improve customer experience.

Aspect-Based Sentiment Analysis aims to extract both the sentiments and the aspects. Aspect-Based Sentiment Analysis categorizes your customers’ feedback based on aspects and identifies sentiments associated to them creating a very fine-grained sentiment analysis task.

Aspects, here, refers to the attributes or components of a product or service e.g. “the user experience for a particular service,” “the delivery time of an order,” or “the feedback on a particular product. The attributes could vary from a feature of the product, ease-of-use of the product or seamless navigation from browsing to placing an order to the last-mile efficiency to deliver the product.

Consider an example review.

“The flowers were beautiful, but it was delivered late. However, the delivery person was very graceful.”

We can see that the review has two components – the sentiments – “beautiful”, “late” and “graceful” – and the entities/aspects described in the text – flowers, delivery time and behaviour of the delivery person.”

Categorizing your reviews based on aspects allows you to quickly find out which aspects your customers are talking about most often. Aspect here highlights some less noticeable features that, nonetheless, can play a significant role in the overall process, making the company perform better.

This enables every team, from customer support to product development, make intelligent and informed decisions by analysing sentiments on tags pertaining to their team and gain valuable, granular-level insights. So, instead of second-guessing what might frustrate or satisfy customers, you can make decisions based on cold hard facts.

How Saksoft helped a customer benefit from Aspect-based Sentiment analysis

The data science team at Saksoft recently worked on a concept for one of the world’s largest flower delivery network that analysed feedback from customer reviews because reviews are highly focused with little irrelevant information.

Two separate models were built – one for Sentiment analysis and another for Aspect Category Detection (ACD) – and combined them to extract the sentiments of aspects.

Aspects can vary between datasets and domains. A good method to identify aspects and sub-aspects is by creating custom aspects for the dataset in hand by leveraging text analysis methods. A machine learning model was taught to tag each review with custom aspects identified by transformers. The aspects, ranging from Service to Price, were further broken down into sub-aspects. For example, the Product aspect further had sub-aspects such as flowers, chocolates, etc.

The reviews were then analysed enabling the team to have a better insight by singling out the areas/aspects that needed to be improved.

Aspect-based sentiment analysis is trending ahead in significance, giving businesses the lead to listen to customers, understand their feelings, analyse their feedback, and improve customer experiences, besides rolling out measures to meet their expectations for your products/ services. With aspect-based sentiment analysis, you take a step further to sharpen customer-centricity and assure delightful customer experiences.