Looking for valuable insights in a sea of user feedback and customer service correspondence can feel like searching for a needle in a haystack. You see heaps of data, but you don’t know how to sort through it all and make sense of what you find.
That’s where sentiment analysis can help. With the right process, all that data can be transformed into valuable, actionable insights you can use to improve the customer experience.
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.
Summary
Follow this six-step sentiment analysis framework:
1. Gather textual data from places like social media posts, online reviews, and customer feedback surveys
2. Clean your data and remove elements like emojis and other unicode characters so sentiment software can analyze it
3. Analyze your dataset with tools that have Natural Language Processing (NLP) abilities
4. Share your insight with key team members and stakeholders
5. Use the insight to improve your product, increase positive sentiment, and strengthen brand reputation among customers
6. Test your changes and validate your findings
Common challenges of sentiment analysis include:
Context can be lost in text data, impacting the sentiment score
Emojis convey additional meaning, but sentiment analysis tools can’t process them, so removing emojis can result in losing important nuances
Double negatives can confuse sentiment analysis algorithms
Idioms may also confuse AI software
AI software interprets text literally, making it difficult to accurately analyze irony and sarcasm
6-step sentiment analysis framework
Follow this framework to learn more about your users and the tasks they want to complete with your product:
1. Gather textual 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 comments, online customer reviews, and customer support conversations.
You also need to gather insights directly from your customers. 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. This can help you collect user responses with varied sentiments.
Pro tip: use Contentsquare’s survey templates (found within our Voice of Customer tool) to get the most out of your research. Using a template with tried-and-tested questions saves you time designing surveys, while letting you learn exactly what you want about your product from your users.
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 data 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 dataset
Now that your data is clean and organized, it’s time to analyze it. Your approach to analysis will vary depending on the nature of your data.
If you’ve collected surveys with open-ended questions or want to analyze social media mentions, online product reviews, and customer service conversions, user sentiment analysis software can help. Sentiment analysis tools use natural language processing (NLP)—a subset of AI—to analyze emotions in text.
Some useful tools include:
Awario to track and analyze reviews
Babel Street for multilingual text analysis
Repustate for video analytics
Critical Mention for television and digital media monitoring
Qualtrics XM + Clarabridge for social media monitoring and customer support analysis
When analyzing your data, go beyond the overall sentiment of your users. Use tools that can identify and segment by topic, so you know exactly what it is about your product that users can’t stand and what they can’t get enough of.
4. Visualize and share your insights
Your insights are the most valuable 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 let you 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.
Pro tip: use the Contentsquare + Slack integration to keep members across your company in the loop. Send feedback and survey responses in relevant channels to keep your team aligned.
The clearer your findings, the easier it will be to get decision-makers on board with your recommendations. So, ensure you display the sentiments behind your users’ opinions. Additionally, dig deeper into the causes of these emotions and identify the changes needed to improve the user experience.
For example, Session Replays help you understand the reasons behind your users’ sentiments. Watch recordings from users who give negative feedback to pinpoint what they struggled with so you can find a solution.
5. Put your findings into practice
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 and identify which changes would be the most impactful.
For example, if you identify negative sentiments about customer support response times, you can prioritize changes to streamline your support process.
Or, maybe you discover there’s a bug that’s causing users to have a negative experience. You can then use Contentsquare’s Session Replays to identify where the bug occurs so you can correct it and enhance the customer experience.
6. Test your changes, validate your learnings, and continue iterating
User sentiment analysis helps you identify which parts of your product need adjustment, but the only way to determine whether your changes have a positive impact is to test them.
For example, use the Contentsquare + Optimizely 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.
And remember: 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. Continue monitoring user sentiment over time and hypothesizing ways to improve it.
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:
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 may reflect a negative sentiment in another. For example, tech-savvy customers might describe a product as ‘highly-technical’ which could have positive sentiment for this demographic. Whereas novice customers may use the term ‘highly-technical’ in a negative fashion.
Emojis: emojis can often change the meaning of a piece of text, and sentiment analysis tools often miss these nuances. Especially when examining the sentiment of posts on social media platforms like Twitter and Facebook. e.g., “This tool is 🔥” and ‘“This tool is 👎”
Negations: double negatives (e.g., “I don’t dislike the new update”) can confuse a sentiment analysis algorithm, unless it knows how to identify and interpret them.
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’ may confuse AI software.
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 data and customer feedback, 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).
FAQs about sentiment analysis process
What is sentiment analysis and how does it work?
Sentiment analysis is the process of using artificial intelligence (AI) to examine and classify customer sentiment and emotions in a piece of text, like feedback gathered from surveys. It breaks down content into topic chunks and assigns a sentiment to each of those sections, such as ‘positive’, ‘neutral’, or ‘negative’.
What are the benefits of sentiment analysis?
Sentiment analysis helps you understand and draw user and customer insights from a large amount of data in an organized manner. With this data, you can improve your product and create a better customer experience. You can also use sentiment analysis for market research to understand how customers feel about your competitors.
What are the steps of the sentiment analysis process?
The steps in the sentiment analysis process include gathering data, sorting and cleaning data, analyzing data, visualizing and sharing insights, and applying your findings. Another important step that people often overlook is testing changes to see if they have a positive impact on the user experience.