6 steps to effectively analyze user and customer sentiment

This article gives you a six-step framework to carry out an effective sentiment analysis process and discover how your users truly feel about your product, so you can make changes or optimizations that drive growth.

We also cover the challenges of this process, so you’ll know what to look out for and how to make the most of your sentiment analysis to improve your product and the user experience (UX).

A flexible 6-step framework for your sentiment analysis process

Sentiment analysis can be a powerful resource to help your product, marketing, customer success, and sales teams learn more about your users and the tasks they want to complete with your product.

Our six-step chronological framework shows you how to analyze user sentiment from start to finish and collect valuable, actionable insights, so you have a powerful process in place to make sense of all your findings.

Let's go!

1. Gather your data

First, you need a rich pool of customer data to conduct sentiment analysis. Fortunately, much of that data already exists in the form of social media posts, online reviews, and customer support conversations. You also need to gather insights directly from your customers to use in your analysis. 

Use surveys and feedback to gather insights from your customers while they’re on your website or using your product. For example, a net promoter score®(NPS) survey gives you quantitative insight into how your users feel about your product. Then, you can get a deeper understanding of user opinions by conducting sentiment analysis on the reasons they provide for their scores. 

Keep in mind that negativity bias can sometimes skew the responses you get from your feedback and surveys. People tend to fixate and report on negative sentiments, which can make dissatisfied users more likely to respond to your surveys than satisfied users. 

To prevent this, offer incentives to users who fill out your surveys, like a discount on their next purchase or subscription, so you know you’re getting responses from users with varied sentiments. 

2. Sort and clean your data

Before you can analyze your data, you need to clean and sort it. Many sentiment analysis algorithms are incapable of interpreting emojis and other unicode characters, and text needs to be ‘normalized’, or standardized, for AI to understand and analyze it. 

Using a text-cleaning and sentiment analysis tool like Repustate, or alternatively processing your text with Python code, helps you clean your data and remove punctuation, emojis, and stopwords. 

Additionally, if you have any video or audio data, you (or a specialized tool) need to transcribe it. Likewise, if you use a tool without multilingual capabilities, you’ll have to translate your content. 

Be aware of the challenges that responses written in unique dialects and vernaculars can pose for sorting and analyzing your data. If you have users who speak in uncommon vernaculars, be sure to use sentiment analysis software (more about this below) that is capable of learning new dialects, so you can get more accurate results.

3. Analyze your data 

Now that your data is clean and organized, it’s time to analyze it. Depending on the nature of your data, your approach to analysis will vary. 

If you’ve collected surveys with open-ended questions or want to analyze social media mentions, online reviews, and customer service conversions, user sentiment analysis software can help. 

Some useful tools include: 

• Awario to track and analyze reviews

• Rosette for multilingual analysis

• Repustate for video analytics

• Critical Mention for television and digital media monitoring

• Qualtrics XM + Clarabridge for social listening and customer support analysis

When analyzing your data, don’t just pay attention to the general sentiment of your users’ opinions. Rather, use tools that can identify and segment by topic, so you know what exactly it is about your product that users can’t stand and what they can’t get enough of.

If you’ve collected survey and feedback data with Hotjar (that's us👋), use the Dashboard feature to view and categorize your results on one consolidated platform. Then, use filters to organize your Heatmaps, which show you where users click and scroll, and Session Recordings (playbacks of users navigating your product) into different categories like loyal users vs. users who churn. This lets you quickly identify patterns and dig deeper into the specific feedback each type of user leaves. 

4. Visualize and share your insights 

Your insights are of most use when you share them with the right people, which is why cross-functional collaboration is an essential step in the sentiment analysis process. Many sentiment analysis tools, like Hotjar's Dashboard feature we mentioned above, allow you to create helpful visuals, like graphs and charts, to clearly present your findings to senior management and stakeholders. 

And while you’ll certainly want to share these insights with other members of your team, you’ll often have to communicate results with members of other departments. For example, a marketing team may find insights related directly to a product’s functionality that they need to share with product designers and developers to fix the issue. 

The clearer your findings, the easier it will be to get decision-makers on board with your recommendations. So, be sure to not only display the sentiments behind your users’ opinions, but also dig deeper into what’s causing these emotions and what changes need to be made to improve the user experience. 

For example, session recordings help you understand the reasons behind your users’ sentiments. When a user gives you negative feedback, go back and watch their recording to pinpoint what exactly they struggled with so you can find a solution. 

5. Put your findings into practice

Improving your product and the user experience should be your ultimate goal when performing sentiment analysis—so, one of the most important steps in this process is implementing the changes you identify from your research findings. 

The great part about sentiment analysis tools is that they often categorize the specific aspects or features of your product or service that evoke negative sentiments, so you can get right to the root of the issue. For example, if you identify generally negative sentiments about response time for customer support queries, optimizing your customer support team or using a tool like Zendesk helps turn frustrated customers back into delighted users.  

Or, maybe you’ll discover there’s a bug that’s causing users to have a negative experience. Use Hotjar Recordings to identify just where that glitch occurs so you can correct it and enhance the customer experience. 

6. Test your changes and validate your learnings

User sentiment analysis helps you identify which parts of your product need adjustment, but the only way to determine whether your changes are having a positive impact is by testing them.

For example, create heatmaps when carrying out A/B testing on your product updates, and examine whether your improvements keep users engaged or have little positive impact. Use the Hotjar + Google Optimize integration to conduct A/B tests with different versions of a feature or design choice, then use heatmaps to understand which version is more successful and why.

Or, if you reinforced your customer success department as a response to negative sentiments surrounding it, send out a time-to-resolution survey to gauge the effectiveness of your efforts.

Sentiment analysis is not a one-and-done process—it should be a key part of your overarching approach to improving your product and ensuring customer satisfaction. 

5 challenges of sentiment analysis (and how to overcome them)

While sentiment analysis is extremely valuable for your business and customers, it comes with its challenges, which mostly relate to the difficulty of getting computers to understand human emotion.

Keep an eye out for some of these difficulties of sentiment analysis: 

1. Context-dependent errors: the meaning of a sentence is often determined by its context. In one context, it can have a positive connotation, while it reflects a negative sentiment in another. 

2. Emojis: especially when examining the sentiment of posts on social media platforms like Twitter and Facebook, emojis can often change the meaning of a piece of text, and sentiment analysis tools often miss these nuances.

3. Negations: double negatives can confuse a sentiment analysis algorithm, unless it knows how to identify and interpret them.

4. Idioms: many sentiment analysis tools only understand the literal meaning of text, so sayings like 'on the ball' or 'costs an arm and a leg' will confuse AI software.

5. Irony and sarcasm: backhanded compliments and other uses of irony and sarcasm can be confusing to sentiment analysis algorithms, as they may interpret them literally.

You can improve your sentiment analysis results by choosing tools that understand context and ones you can train to recognize idioms and emojis.

Create a brilliant customer experience with sentiment analysis

After going through the hard work of collecting feedback and data from your users, you need an efficient way to see the deeper meaning behind it.

Following these sentiment analysis steps helps you collect insights, understand how users feel, share your findings, and make informed decisions to enhance your product and the customer experience (CX).