User sentiment analysis: what it is and how it benefits your customers and business

User sentiment analysis helps you sort through and make sense of the unstructured data you collect—via feedback forms, interviews, reviews, etc.—to understand what customers think and how they feel at all stages of the buyer journey—and beyond, so you can segment your users, address issues, and improve your product to boost loyalty and satisfaction. 

This guide walks you through the fundamentals of user sentiment analysis—what it is, why it matters, how to analyze sentiment, and the challenges that come with it—to improve your product and reach your business and customer goals. 

What is user sentiment? 

User sentiment is the positive, negative, or neutral thoughts and feelings customers have about your brand or product. 

For example, if a user says, “Amazon Prime’s documentary selection isn’t as good as Netflix,” they’re expressing negative sentiment about Amazon Prime. Or if they say, “The interface on my new phone is hard to navigate,” you know they’re not 100% happy with the user interface (UI) design. 

However, it’s not clear—in either of these cases—exactly which emotion the user is feeling. That's why analyzing user sentiment is key to gauging their level of emotional reaction and connection to your product—and improving the customer experience (CX). 

These days, customers express their thoughts and feelings over a variety of channels:  

• Customer feedback: like online customer satisfaction surveys where users tell you exactly what they love and hate about your product and the customer experience

• Chatbot or help center interactions: including call transcripts and emails to customer support teams. For example, if users are frustrated because they can’t find essential information, their messages are likely to express that emotion.  

• Sales calls: conversation analysis tools can reveal uncertainty in a potential customer’s words, even if their hesitation is too subtle for a human to detect from their tone 

• Online reviews: these are a goldmine of customer sentiment as people don’t hold back from expressing themselves

• Social media: through posts and comments on popular online platforms. For example, if you post about your latest update, a user might comment that they’re annoyed by the price hike

Why do you need to perform user sentiment analysis? 

User sentiment analysis is the process of extracting objective, actionable insights from subjective, unstructured data. Analyzing user sentiment involves using machine learning and natural language processing (NLP)—speech recognition, natural-language understanding, and natural-language generation—to recognize and understand emotions in speech and text. 

Analyzing and acting on user sentiment is key to helping you achieve your business goals, improve CX, and boost customer satisfaction and loyalty.

The benefits of analyzing user sentiment are:   

• Connect the dots on customer satisfaction scores to reveal what customers think and feel about your website, brand, and digital products—and respond with small changes that have a big impact.

For example, analyzing feedback from an unsatisfied mobile user can pinpoint whether they’re confused, frustrated, or even planning to switch to the competition. This lets you prioritize changes and actions to address the issue. If they’re just confused about how to access certain features, you can fix that by improving your interface or product tour. If they’re angry enough to churn, however, you might want to reach out with an apology, discount, or other incentives to retain them.  

• Understand the why of customer behavior and not just the what (the actions users take on your site) to understand your customers' motivations, pain points, and challenges.

For example, if you're using Hotjar (that's us👋), watch session recordings of users navigating your website to find out exactly where they drop off. What they won’t reveal is what users were thinking and feeling right before they abandoned the page. So, combine session recordings with insights from feedback widgets to put two and two together and make quick-win changes to reduce drop-offs. 

• Bring the voice of the customer (VoC) to your decision-making to align cross-functional teams, eliminate disagreement about ‘what the customer wants,’ and provide valuable insights to convince stakeholders. When you empathize with your customers in this way, you stop relying on guesswork and instead make decisions based on hard data—which puts you in a better position to achieve product-market fit and growth.  

• Gauge users’ level of emotional connection to your product: this is an indicator of loyalty, repeat purchases, and recommendations. Customers who love your product are more likely to renew and recommend it to others—user sentiment analysis lets you identify and target these customers with marketing campaigns and incentives to boost referrals. For example, give a discount for every three new customers that sign up using a referral link. 

• Spot patterns and trends: monitoring social media posts lets you gauge public opinion and determine if your marketing campaign is missing the mark, so you can adjust it in time

• Improve and scale data processing: user sentiment analysis tools speed up the time-consuming process of categorizing and analyzing masses of data to give you more reliable insights, faster

How to perform user sentiment analysis in 5 steps

There are five basic steps in the user sentiment analysis process: 

1. Gather your data: the sources and tools you use to gather data will depend on your objectives and customer base. For example, if you’re a B2C retailer looking to gauge public opinion after launching a new campaign, you need to monitor social media. But if your B2B SaaS company wants to understand how customers feel about your onboarding process, place customer surveys at strategic points in your app. 

2. Sort, process, and clean your data using dedicated tools: for example, if you’re a B2B sales organization wanting to improve customer interactions, you can use transcription software to transcribe calls to your customer-facing teams. Then, remove stopwords, incorrect punctuation, etc., to make your data easier to analyze. 

3. Analyze your data using user sentiment analysis software. For example, AI-based software like Lumoa generates an overall customer sentiment score, which serves as a key performance indicator (KPI) you can track. When the score drops below acceptable or benchmarked levels, read and look at individual customer feedback to find out why. 

4. Visualize and share your insights: create actionable reports with visuals (graphs, charts, etc.), or use tools like Hotjar’s Dashboard to see what’s happening in your product at a glance with all your meaningful insights on one page, to present your findings to team members and stakeholders.

5. Put your findings into practice: examples of user sentiment analysis in action include: 

• Improving product listing pages and removing barriers to conversion on your ecommerce website. For example, by clarifying information that was confusing people, or making returns policies more accessible at checkout. 

• Providing call center operators with a customer sentiment score and history of previous interactions: this helps route calls to the most appropriate operator and offer relevant solutions

• Enacting damage limitation measures if a prominent sponsor’s social media comments are poorly received by your audience

• Running A/B testing on a SaaS website to see which versions of your pages and CTAs convert best

What are the challenges of user sentiment analysis? 

Of course, like anything that involves human emotions, sentiment analysis comes with its challenges. 

At the planning stage of sentiment analysis, challenges include: 

• Developing a hypothesis, working out what data you need to test it, and which customer segments you need to target to get the information you need

• Knowing which questions to ask: these should always be open-ended, so users can tell you what they think and feel in their own words. Check out our piece on survey question examples for inspiration. 

• Figuring out which methods you need to use to collect data, whether it’s customer interviews, surveys, call transcripts, etc. 

Data collection comes with its own challenges: 

• Achieving a large enough sample size, like collecting enough data to test your hypothesis. It’s not always easy to persuade people to answer surveys, and customer interviews especially can be time-consuming and costly to run. 

Use feedback widgets or NPS surveys that ask customers to provide a rating and overcome this challenge. Then, follow each rating request with a longer, open-ended question. This way, it feels like less effort.

• Monitoring user sentiment can be time-consuming: you need a system to track and analyze data, which can be complex and costly. It can also be challenging to get accurate and reliable data, particularly if you’re monitoring a large number of users.

And the challenges don’t end once you’ve collected data: 

• Analyzing hundreds of data sources is time- and resource-intensive: to overcome this, invest in user sentiment analysis software to do the heavy lifting or use this spreadsheet

• Understanding context is challenging, especially for machines: it’s difficult to detect irony or sarcasm from text alone. Curse words can also completely alter the meaning of a sentence.

• Emojis: customers frequently use emojis on social media and, increasingly, other sources. NLP tools are trained to be language-specific, whereas emojis have their own language and their use changes all the time (as any confused parent of teenagers will tell you).

• Results can be taken out of context and lead to the wrong conclusion: review individual responses to truly understand how a customer feels about your brand and product experience. Observation bias and subjectivity also impact interpretation. Even with the best will in the world, the unconscious biases we all suffer from can distort data collection and interpretation.

• Foreign languages: if you’re analyzing text in a language you don’t speak well, you may miss nuances. But, for many global brands, analyzing multilingual customer feedback is a must.

Dig deep into the what and the why with user sentiment analysis

It’s not enough to collect feedback; you need to be able to understand your user's sentiment behind their words. Only then can you understand why customers do what they do when they interact with your website or product. 

To perform user sentiment analysis, first, identify what data you need to collect, and from which customer segments and sources. Then, combine PX tools and customer research with qualitative data analysis tools to better address customer issues and improve your product to boost loyalty and customer satisfaction.