Comment Sentiment Analysis

Customer feedback can provide valuable insight into how to improve a product or service, but often it can be difficult to glean the overall sentiment behind each comment. However, by incorporating machine learning and natural language processing, this task becomes much easier.

This new feature automatically detects the tone of each comment and organizes them accordingly, giving businesses the opportunity to prioritize and address specific issues with their customers. In addition, by also identifying comment topics, businesses can quickly identify patterns and common concerns among their customer base.

Utilizing this technology not only saves time for businesses, but it also helps to ensure that customer feedback is accurately captured and addressed in a timely manner. Overall, this new feature adds an essential layer of analysis to customer comments, providing crucial information for improving a company's offerings.

Project Team

UX Manager / Designer

1 Product Owner

2 Data Analysts

2 FEDs

My Role

UX Manager / Designer

Tools

FIGMA

Tones

The new Comment Tone Summary chart allows you to see your percentage of satisfied customers at a glance. This chart now breaks your comments down by tone, so you can quickly get a gauge on your customers’ experience during any customizable timeframe. Clicking each tone total expands into user demographics and more.

Trends

The new Comment Trends graph allows you to see how your comment tones are trending over time. As you make changes and improvements to your site and user experience, you’ll be able to see to what extent these changes affect the user satisfaction trend line. Additionally, you may see dips and peaks in this graph which are tied to seasonal shopping, a holiday weekend, a special sale or other event. This will empower you to predict such changes and account for them in your user journey.

Topics

The new Comment Topics table shows your comment tones broken into totals. Clicking each comment total takes you directly to your comments page. This qualitative survey data can help you understand  why your comment tones are trending in a certain direction, as well as user satisfaction.