Reviews are a treasure trove of information. That’s where your customers tell you exactly what they do and don’t like about your company. Large organizations typically have vast quantities of reviews that are packed with valuable insights.

Making sense of these requires a systematic approach. Review sentiment analysis using AI tools helps you unpack what your customers feel about particular topics and track how that changes over time.

Once you have these insights you can leverage them to improve your products and services or create new products that better meet your customers’ requirements. You might introduce new features that customers have asked for in their reviews. Or you could use the data to identify exactly what needs to change to improve the overall customer experience. Organizations that understand their customers can make data-driven decisions that actually meet their needs.

In this article we’ll explore why sentiment analysis of reviews matters to your organization, and what you need to do to get started with your own program. We’ll also look at how to choose the right AI-powered tools for performing sentiment analysis and our top practical tips for getting better results from your data.

Why Sentiment Analysis of Reviews Matters

Every business loves 5-star reviews. But the real gold is in your customers’ comments. Customer sentiment is the feelings, emotions, and attitudes customers have about a particular organization or their products and services.

Digging into the feelings and emotions behind online reviews tells you where your organization needs to focus. Sentiment analysis is a powerful tool for improving products and services and increasing customer satisfaction.

Let’s look at some real-world examples of how businesses are using sentiment analysis on reviews:

Uncover Customer Satisfaction Trends

Businesses use sentiment analysis to track how their customers are feeling over time. Sentiment Score is a useful metric for monitoring overall customer satisfaction.

If you’re using an AI-powered sentiment analysis tool you can set up automatic alerts if the Sentiment Score changes significantly. Think of it as an early-warning system allowing you to address any urgent issues as they emerge.

Reveal Strengths and Weaknesses of Products or Services

Sentiment analysis is also useful for understanding what your customers do and don’t like about your products. It can reveal hidden issues that you didn’t know about. These insights are invaluable for targeted product validation and improvements.

Real-World Example: How Levels Uses Review Sentiment Analysis to Build Better Products

US-based health and wellness company Levels helps people see how food affects their health. Users wear a bio-sensor which tracks blood glucose levels to show which foods impact them most. Levels needed their product to delight early adopters and generate positive reviews.

Levels used Thematic to automatically make sense of the thousands of text feedback responses they received. This approach provided contextual understanding of themes in the data and made it easy to identify key pain points. Engineers and product teams quickly grasped the subtleties and context of important issues, and knew where to focus.

Improve Customer Experience

According to research by PwC, 59% of customers walk away after several bad experiences and 32% would leave after just one bad experience. Sentiment analysis helps you better understand what you need to do to reduce churn and boost customer loyalty.

AI sentiment analysis tools allow you to drill down into a particular theme in your reviews and see how your customers feel about it. In the example below 88% of mentions of “out of stock” are negative. We know this is an important issue for customers since it’s mentioned in 11% of reviews. Fixing this issue should make your customers happier and ensure they keep doing business with you.

Track Competitors & Identify Opportunities

It also pays to keep an eye on your competitors. Analyzing their reviews gives you a better picture of their strengths and weaknesses. And it also enables you to identify potential opportunities and gaps in the market.

For example, you might notice that many of their customers complain about a particular feature that’s missing from their products. Addressing this gap could help strengthen your market position. These insights can also be invaluable for building tailored marketing campaigns.

How to Conduct Sentiment Analysis on Reviews

Traditionally sentiment analysis was done manually by reviewing comments and categorizing them. Today most analysts use AI-powered tools that can accurately analyze thousands of reviews in seconds.

Let’s take a quick look at the main differences between manual and automated sentiment analysis.

Manual Review Sentiment Analysis

Sentiment analysis used to be a painstakingly slow and manual process. Analysts went through huge datasets and classified sentiment by hand. They developed sets of rules that made it easier to categorize the text correctly based on positive and negative words.

For example, a very simple rule could be “if a sentence contains the word ‘great’, then the sentiment is positive”. This approach is called rule-based sentiment analysis.

Automated Review Sentiment Analysis

More recently AI-powered tools like Thematic have harnessed LLMs (Large Learning Models) to automate sentiment analysis. These solutions allow analysts to process huge quantities of reviews and get detailed insights. LLMs can pick up on context and even interpret humour and sarcasm to correctly determine sentiment.

Aspect-Based Sentiment Analysis

Traditional document-level sentiment analysis focuses on the overall sentiment of a text, whereas sentence-level sentiment analysis takes a more granular approach. Sentiment analysis is most useful when it’s tied to a specific attribute or a feature described in text.

The process of discovery of these attributes or features and their sentiment is called Aspect-based Sentiment Analysis, or ABSA. Aspect-level sentiment analysis determines whether the sentiment connected with a particular theme in a review like “processor speed” is negative, positive, or neutral. This process can be automated using AI tools.

How Automated Review Sentiment Analysis Works

1. Data Collection: The first step is to connect your review data to your chosen platform. Most leading AI-powered analysis tools like Thematic have integrations that easily allow you to pull reviews from platforms like Trustpilot, Tripadvisor, or G2. Consider that different review channels will likely offer varying viewpoints and types of data. At this stage it’s a good idea to think about your business goals and what type of information you want to capture

2. Text Processing: Reviews are unstructured data so they need to be preprocessed to ensure accurate sentiment analysis. AI tools automatically clean the data by removing things like special characters, URLs, and spammy content. Emojis and slang also need to be dealt with since they’re often key indicators of emotion. For example, “😊” usually indicates positive sentiment.

3. Sentiment Classification: The data is now ready for analysis. Machine learning models are trained on vast datasets to help them determine whether text indicates positive, negative, or neutral sentiment. LLMs can also accurately distinguish specific emotions like frustration, anger, and annoyance.

Example: Automated Review Sentiment Analysis for E-commerce Sites

E-commerce sites like Amazon have huge quantities of reviews. They simply can’t analyze this valuable data manually. Automated sentiment analysis solutions allow them to quickly understand what their customers need and fix urgent issues fast.

Automated Review Sentiment Analysis vs. Manual Review Sentiment Analysis

Combining AI and Human

The best option is to combine both human and AI-powered sentiment analysis. This helps ensure greater accuracy and relevance.

Thematic’s platform allows you to go in and make manual tweaks to the analysis. For example, you can introduce your own themes or topics that the AI may have overlooked. Or you can remove any themes that aren’t relevant to you.

Key Features to Look For in Review Sentiment Analysis Tools

With so many AI-powered analysis tools on the market, choosing the right one for your organization can be tricky. Large organizations with lots of customer reviews will likely need a more sophisticated AI solution than smaller businesses with less complex needs.

Here’s some useful criteria to help identify the best tool for your requirements:

AI Analysis Capabilities

Different tools have different capabilities. Some tools are good at performing a simple sentiment analysis task on smaller datasets, whereas advanced tools offer a more comprehensive solution.

Think about what types of insights your organization needs. If you need a high level of granularity and detail, consider investing in a more sophisticated platform. It’s a good idea to test out the AI analysis capabilities of different tools to understand what will work best for you.

You can try out Thematic on your own data with our free guided trial.

Customization and Flexibility

Each business has unique needs and requirements. Check that you can customize the tool to work best for you. For example, with Thematic you can edit and delete “themes” or topics with the dedicated Theme Editor tool. This ensures greater accuracy and improves the quality of your insights.

Multi-Channel Integration

Strong integration options simplify connecting your data to your chosen platform. Select a tool that makes it as easy as possible to get all the data you need from review sites like Trustpilot and G2 in one place. Most popular tools offer built-in integrations to major review sites.

Multi-Language Support

If you receive reviews in multiple languages, you’ll need a tool that can analyze customer feedback across different languages and dialects.

Ease of Use

Tools that are intuitive and easy to master reduce labor and training costs. Look for tools that offer user-friendly interfaces and accessible onboarding. Consider whether non-technical teams will need to use the tool and what they need to get started quickly.

Pricing

Pricing is often a critical factor in choosing the right tool for your organization. The best solution is one that fits your budget without sacrificing essential features. Look for platforms that offer flexible pricing plans that are tailored to your organization depending on how much data you have. This way you only pay for what you use.

Real-Time Analysis

Consider how important real-time analysis is for your company. Being able to instantly get alerts about upticks in negative reviews can be invaluable for some companies.

For example, if you have just launched a product you’ll want to keep on top of any issues that could impact customer uptake and harm the success of your launch. Getting almost instant insights allows you to start fixing the problem as soon as possible.

Top Tools for Review Sentiment Analysis

Once you’ve defined your key criteria, it’s easier to choose the tool that has the right features, capabilities and pricing options for your organization. In this section we’ll take a quick look at three top AI sentiment analysis tools for analyzing your review data.

Tool #3: Lexalytics (InMoment)

Lexalytics, which is now owned by CX suite InMoment, combines Natural Language Processing techniques (NLP) with Machine Learning to analyze sentiment. The platform enables users to monitor and analyze reviews and ratings from over 100 sources.

One particularly useful feature is Lexalytic’s ability to detect customer intention in review data. The tool can pick up whether customers intent to leave your brand or to recommend you to others. This information can be handy if you want to boost brand loyalty and customer retention.

Tool #2: Zoho Analytics

Zoho Analytics is part of the Zoho suite which can be a good option for those already using the platform. Zoho provides entry-level analytics tools, including sentiment analysis, been designed with small and medium-sized businesses in mind.

Zoho’s sentiment analysis offering is provided by Zia and uses Machine Learning. It can perform simple sentiment analysis tasks like picking up on negative words in reviews so these can be prioritized.

Tool #1: Thematic

Thematic is an advanced AI thematic and sentiment analysis tool. Thematic works by automatically identifying themes, or topics, in your customer reviews and layering this with sentiment analysis. It’s a great option for medium and large companies needing a more sophisticated understanding of customer sentiment.

In the example above you can see how Thematic combines both thematic analysis and sentiment analysis to dive deeper into customer reviews. You can see exactly how many negative, positive and neutral mentions there are of the theme "print boarding passes" in the reviews.

In this case you can see that 61.2% of mentions have negative sentiment and just 35.3% have positive sentiment. This allows you to gauge the overall customer sentiment linked to this topic and assess what action needs to be taken.

Challenges and Considerations in Review Sentiment Analysis

Applying sentiment analysis to reviews comes with unique challenges. In this section we’ll look at the most common challenges businesses often face and how we recommend dealing with them.

1. Managing Unstructured Data

The Challenge: Reviews come in lots of different formats. You might have very informal comments from sites like Facebook or highly detailed and technical reviews from sites like G2. This unstructured data is riddled with slang, emojis, abbreviations, and spelling mistakes. Traditional sentiment analysis approaches may struggle to process these reviews correctly which can lead to inaccurate insights.

Solution: Advanced AI tools like Thematic preprocess your review data to remove any unnecessary elements and correct spelling errors. They then analyze and categorize your data automatically, pulling out specific topics and identifying the sentiment associated with each topic. This helps to improve the accuracy of the analysis.

2. Handling Multiple Languages

The Challenge: Global businesses receive reviews in multiple languages. This can be challenging since different languages have unique expressions, idioms, and structures. A one-size-fits-all approach does not work when your customers come from many different countries. Sentiment may be misinterpreted due to linguistic or cultural nuances.

Solution: Select AI analysis tools that can analyze reviews in various languages. For even greater accuracy it’s a good idea to train the models you’re using with training data specific to each region or country. This enables the model to learn what certain phrases or contexts mean and get better at identifying the underlying sentiment.

3. Ensuring Accuracy in Sentiment Detection

The Challenge: Sentiment analysis tools can struggle with mixed emotions, irony, and sarcasm. For example, the phrase, “I love the product, but the service was terrible”, contains both positive and negative sentiment. Simpler tools may struggle to correctly identify both the good and the bad. This can lead to inaccuracies and missed opportunities for improvement.

Solution: Advanced AI tools combined with human analysis is the best approach. Harnessing the best of AI and humans increases accuracy and reliability. Humans can review ambiguous reviews and classify them appropriately. It’s also recommended to test your model across different content types and subsets of data. You could conduct bias assessments and A/B testing to reveal any issues.

4. Processing Short or Sparse Reviews

The Challenge: All too often customers leave brief reviews with very little information. Chances are you have lots of reviews that just say things like “Great service” or “Terrible experience”. These reviews don’t have enough data to tell you why your customers feel the way they do.

Solution: Advanced sentiment scoring considers the polarity and intensity of the words. So even if you don’t have much to go on you can still get a sense of how your customers feel. The best approach is to analyze large quantities of reviews so you capture the overall sentiment trends.

Practical Tips for Better Review Sentiment Analysis Results

AI sentiment analysis tools do most of the hard work for you. But for the best results, we recommend combining AI with human intelligence.

Here are our recommendations on practical things you can do to get more accurate and actionable insights.

1. Consider Data Quality

The accuracy of your results also depends on the quality of your data. Think about your customers and what types of reviews they write. You’ll probably find that you have different types of reviews on different review sites. For example, Facebook reviews are more informal than reviews on specialized B2B sites like G2.

The types of reviews you select will depend on your business goals. If you’re a SaaS provider looking to improve your products you will probably want to pull reviews from G2 and similar sites. This should give you specific and detailed insights into what your customers do and don’t like about your products.

2. Automated Analysis and Manual Checking

AI-powered solutions that use LLMs are fast and accurate. But sometimes they do make mistakes or miss themes. This is especially true with large datasets.

It’s a good idea to run your review data through an AI tool and then have human analysts review the results. This way they can pick up on any errors and improve the accuracy of your insights.

3. Use Sentiment Analysis to Track Trends in Reviews

Sentiment analysis of reviews can give you valuable insights into what you’re doing wrong right now. But it’s even more valuable when you track sentiment over time. Taking the temperature with Sentiment Scores tells you how your organization is doing.

If Sentiment Scores drop significantly from one quarter to the next you know it is time to start digging deeper into the data. Monitoring sentiment patterns enables organizations to adapt to shifting customer expectations and emerging issues.

You can see below how Thematic allows you to track sentiment over time. In the example below you can immediately see what had the biggest negative impact on customer sentiment. This makes it really easy for stakeholders to understand at a glance what is influencing key business metrics.

4. Regularly Evaluate Models and Tools

AI tools and sentiment analysis techniques are growing more powerful and capable every day. It’s a good idea to keep an eye on the tools you are using and assess how well they serve you. Look for AI sentiment analysis tools that are regularly updating and improving their models.

Thematic is constantly evaluating and optimizing new LLMs and selecting the best ones for each task. For the best results consider domain-specific sentiment analysis solutions that can be tailored and customized to your industry and your business.

5. Communicate Your Insights and Take Action

Sentiment analysis of reviews is only valuable if you act on the results. We recommend setting up a structure for acting on your insights before you start the analysis. Think about who needs to have access to your findings and what action they will take.

For example, review sentiment analysis might uncover significant delays in handling customer complaints. Your customer service team would need to delve deeper into their operations to discover exactly what was happening and how to address it. Engaging the right stakeholders with effective communication is key to actually getting results from your sentiment analysis efforts.

Leveraging Review Sentiment Analysis to Drive Success

Reviews can be a powerful source of inspiration and insights. But only if they are leveraged correctly. Advanced AI analysis tools make it easy to perform sentiment analysis and understand how your customers feel. And this information helps organizations make better decisions, produce better products, and build lasting customer relationships.

In this article we’ve covered what you need to know to effectively analyze your customer reviews and get high-quality, actionable insights. But remember that sentiment analysis isn’t a one-off.

Best practice is to keep monitoring your reviews so you can stay connected with your customers and adapt to their changing needs and expectations. Stay one step ahead of your customers by anticipating their requirements and taking prompt action on their feedback.

Try Out Thematic on Your Customer Reviews

Ready to try advanced AI-powered sentiment analysis on your reviews?

Why not start right here with a free guided demo of Thematic using your data.