AI sentiment analysis helps you decode the human emotion in text. It enables you to connect with customers on a deeper level than ever before, and discover what they truly like and dislike about your brand.

Organizations are fast realizing the power of including their customers’ opinions in their operations, product roadmaps, and strategic business decisions. According to a recent Deloitte report, 40-50% of respondents listed sentiment analysis as a priority for the near future. Once you know how your customers feel you can make products and services tailored to them, as well as fixing urgent issues you might not have even been aware of.

In this article we’ll walk you through everything you need to know about using AI-powered sentiment analysis in your business. We’ll take a look at real-world use cases and uncover why AI is the best solution for fast and accurate sentiment analysis. And then we’ll go through a step-by-step plan to guide you through getting started with AI sentiment analysis from scratch.

What is AI-Powered Sentiment Analysis?

AI-powered sentiment analysis is the process of using artificial intelligence to decode the emotional tone of textual data. AI tools can accurately analyze vast quantities of data and classify the sentiment of the text as positive, negative, neutral, or on a more granular scale.

Traditional Approaches to Sentiment Analysis

The earliest forms of sentiment analysis were painstakingly slow and manual. Analysts used to trawl through big datasets and classify sentiment by hand. To make it easier to classify emotion in a text, analysts developed rules to capture positive and negative sentiments. For example, a very simplistic rule could be “if a sentence contains the word ‘great’, then the sentiment is positive”.

The arrival of NLP (Natural Language Processing) and Machine Learning made it possible to automate sentiment analysis. The main idea is that a sentiment analysis model learns from examples of text pre-tagged with sentiment. The training data can be created from reviews with ratings or open-ended survey questions linked to satisfaction scores.

How LLMs and Generative AI are used for Sentiment Analysis

Things have come a long way since researchers first started using NLP for sentiment analysis in the early 2000s. The best AI-powered sentiment analysis tools around today use Large Language Models (LLMs) and Generative AI, such as OpenAI’s ChatGPT or Google’s Bard.

These models have been trained on huge quantities of data and are smart enough to even pick up on specific emotions like annoyance or anger. LLMs have made sentiment analysis faster, easier, and more accurate than ever before. All you need to do is enter a prompt.

In the example below you can see a test we ran using an LLM to identify specific emotions. This was the prompt we used: “In one word, what emotion does the customer express here. Can you identify a specific emotion for each example?"

LLMs are likely now more accurate than an average person at recognizing sarcasm, detecting emotional intensity, and even understanding cultural differences in language. But bear in mind that they require careful prompt engineering and are too slow for some applications.

Thematic’s Approach to Sentiment Analysis

Thematic takes it a step further by combining Large Language Models (LLMs) with traditional non-generative AI. Thematic uses LLMs along with sentiment analysis algorithms that are trained on large volumes of data. A unique feature of Thematic is that it combines sentiment with themes discovered during the thematic analysis process. Thematic identifies the sentiment for every theme or topic mentioned in your customer data. This is also known as Aspect Based Sentiment Analysis.

You also have the option in Thematic 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.

Why Use AI for Sentiment Analysis?

Developments in AI have been a game-changer for organizations wanting to analyze large quantities of data. Let’s take a deeper look at the benefits of AI sentiment analysis over more traditional methods.

Scalability

Humans have limits on how much data they can handle. AI sentiment analysis enables organizations to analyze millions of reviews or social media posts. All you need to do is connect your data to an AI analytics platform like Thematic. Once you’re set up, you can start uncovering patterns across all your data and understand which issues keep recurring. You can get a snapshot of sentiment across many channels at the same time. This can be really useful for monitoring brand reputation or tracking changes to overall customer sentiment.

Speed

What used to take analysts months to achieve can now be done almost instantly. This is one of the biggest benefits of AI sentiment analysis. Businesses can now pick up on sentiment shifts in real time and act accordingly. For example, if you had a big spike in negative sentiment on a critical topic like “data leak” you could immediately get the right teams to fix the issue.

Accuracy

AI-powered insights also win when it comes to accuracy. Recently we tested ChatGPT against traditional rule-based sentiment analysis to see which was more accurate. As you can see below, ChatGPT classified sentiment correctly in 100% of the sentences compared to just 30% for rule-based sentiment analysis.

On the flip side, LLMs like ChatGPT are prone to hallucinating or generating nonsensical content. And they can also struggle to handle large datasets consistently and accurately. ChatGPT can be a great tool for analyzing feedback in small one-off surveys, but it hits many limits for large-scale analysis and reporting.

That’s where it pays to have a more comprehensive analytics solution like Thematic that combines many LLMs with traditional non-generative AI.

Contextual Understanding

AI sentiment analysis also wins when it comes to taking context into account. Traditional rule-based models may struggle with complex concepts like sarcasm or terms like “badass” which could be classified incorrectly.

AI-driven models using transformer architectures like BERT or GPT are designed to process words in relation to all other words in a sentence. This means they can interpret context more accurately compared with traditional methods that consider words independently.

Main Uses of AI-Powered Sentiment Analysis in Business

AI sentiment analysis is a powerful tool for organizations looking to really understand what their customers think and feel. Here’s some real-world examples of how to use it

Analyzing Online Reviews: Only 1 in 26 unsatisfied customers actually complain directly to the company. But online reviews are a great source of information about how you can improve your services, products, and overall customer experience. AI-powered sentiment analysis can process millions of reviews on platforms like Amazon, Yelp, or TripAdvisor and tell you what your customers do and don’t like. For instance, an e-commerce company might pick up on recurring complaints about product quality or delivery times. Fixing these issues should lead to happier customers.

Social Media Monitoring: Brands can keep an eye on their reputation by using AI to analyze social media mentions. For example, Coca Cola monitors negative mentions on social media to identify any emerging issues their customers are talking about. When customers complained about the controversial New Coke, the company swiftly took action and reassured customers that it took their complaints seriously.

Customer Support Insights: Analyzing customer emails, live chats, and call transcripts can tell you how customers feel about their support experience. For example, you might notice a pattern of complaints about slow response times. Once you know this you can respond by rethinking your customer support processes or hiring more staff.

Process Large Quantities of Customer Feedback: Organizations often have more feedback than they can handle. Leading software company Atlassian has over 250,000 customers and a vast amount of valuable feedback to make sense of. The company chose to work with Thematic to unlock valuable insights from all this data. They were able to connect all their feedback channels to the platform and get insights into what their customers really wanted from their products. For example, the feedback revealed how users expected to be able to use AI for adoption to bring new users up to speed faster.

Getting Started: Steps to Implement AI Sentiment Analysis

Starting from scratch with AI sentiment analysis can be daunting, especially if you have large quantities of multi-channel data or urgent customer issues you want to address. In this guide we’ll walk through the 4 main steps you need to apply AI-powered sentiment analysis to your own data.

Step 1: Define Your Goals

The first step is to define what you want to achieve. At this stage you will probably have a range of opinions and requirements from different stakeholders and departments. Distilling these into a clear framework of objectives will help shape how you approach sentiment analysis. You can rank these objectives by priority and urgency so you can focus on what matters the most to the business.

Here’s some examples of objectives you might want to achieve using AI sentiment analysis:

• Improve customer satisfaction by identifying and addressing the issues having the biggest negative impact on metrics like NPS (Net Promoter Score) or Sentiment Scores.
• Track customer responses to a new product or service launch and identify areas for improvement or development.
• Monitor your brand reputation on social media and news channels.

It’s a good idea to leave some flexibility in your plans for any unexpected discoveries you make along the way. For example, sentiment analysis might uncover important issues you weren’t aware of before. Keeping an open mind and agile approach will enable you to respond quickly.

Step 2: Select an AI Sentiment Analysis Tool

Choosing the right AI sentiment analysis tool can be tricky with so many options currently on the market. We recommend getting clear on exactly what you need to achieve and looking for the solution that best matches your budget and technical requirements. For example, larger organizations with significant quantities of complex unstructured data will need a more advanced AI solution.

Here’s some AI sentiment analysis tools for different types of organizations:

Thematic specializes in advanced thematic and sentiment analysis powered by AI. It’s a great option for medium and large companies needing a more accurate understanding of customer sentiment than what’s available as part of the basic CX suite. Thematic works by automatically identifying themes, or topics, in your feedback and layering this with sentiment analysis.

Stratifyd uses natural language processing (NLP) to analyze customer and employee feedback to identify sentiment, key themes, and recurring issues. But since NLP relies on pre-defined rules to determine sentiment, these types of solutions tend to be less accurate than more comprehensive analytics platforms using LLMs and Generative AI.

Zoho Analytics is part of the Zoho suite which makes it a good option for those already using the platform. Zoho provides easy-to-use analytics tools, including sentiment analysis, which have 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 support tickets so these can be prioritized.

Social Searcher is a great option for small businesses who want to monitor their presence on social networks and media sites. While it might not have many advanced features, Social Searcher is inexpensive and easy to use even for non-technical teams. Social Searcher comes with a free sentiment analysis tool where you can check the sentiment behind your online mentions.

Step 2.1: Using ChatGPT for Sentiment Analysis

Earlier in this article we talked about a test we ran to check how accurate ChatGPT is when it comes to sentiment analysis. While ChatGPT is pretty good at sentiment classification, it also has a tendency to hallucinate or generate nonsensical content. And some themes in the test were skipped over completely. Overall, ChatGPT is a great tool for analyzing feedback in small one-off surveys, but it hits many limits for large-scale analysis and reporting.

Thematic offers the best of both worlds since our hybrid AI system combines traditional non-generative and Large Language Models (LLM). We separate analysis into many discrete tasks. This greatly reduces the chances for LLMs to hallucinate and generate incoherent output. And it also allows us to select the right model for each task. Thematic is continually evolving and improving our analytics capabilities by evaluating and optimizing new models as they become available.

Step 3: Gather and Prepare Data

Selecting and gathering the right data matters too. Think about your business goals and what type of information you want to capture about your customers. You might choose to send out surveys to get insights on specific topics. Or you might choose to pull data from online review sites like Trustpilot and G2 or social media.

Bear in mind that different channels will likely offer varying viewpoints and types of data. Social media users may feel more comfortable expressing their views and emotions openly on those platforms while survey responses may be more formal and toned down. For a comprehensive picture of sentiment, it’s recommended to go for a multi-channel approach.

The next stage is preparing your data for AI analysis. This is usually done automatically these days by sentiment analysis tools. They preprocess and clean the data by removing things like hashtags, links, mentions, or emojis.

Step 4: Analyze and Interpret Results

There’s no universal model that can accurately analyze data. That’s why advanced analytics solutions like Thematic use large language models (LLMs) with traditional non-generative AI. Models are auto-evaluated and optimized to make sure the right model is being used for each task.

Thematic combines sentiment analysis with thematic analysis to help you better understand the emotion behind a theme.

In the example above you can see how Thematic combines both thematic analysis and sentiment analysis. You can see exactly how many negative, positive and neutral mentions there are of the theme "store out of stock". In this case you can see that 88% of mentions have negative sentiment and just 5.9% have positive sentiment. This allows you to gauge the overall customer sentiment linked to this topic and assess what action needs to be taken.

💡Thematic Expert Tip: Ask questions about your data using Generative AI

Thematic has a handy tool called Thematic Answers which makes it easy to dig deeper into your data. Users can ask direct questions about customers’ opinions on specific themes and topics. All they need to do is type in their question, and Thematic’s Generative AI will provide the answers.

How to Choose the Right AI Sentiment Analysis Tool

Choosing the right sentiment analysis tool for your organization can seem daunting. Here’s some criteria to consider so you can choose one that best fits your business needs:

AI Analysis Capabilities

Not all AI sentiment analysis tools are made equal. You may need an advanced analytics tool especially if you are a larger organization with significant quantities of unstructured data. We recommend you test out the AI analysis capabilities of different tools to understand what will work best for you. This helpful checklist from Esomar gives you some useful questions to ask when you’re researching AI-powered solutions.

Thematic currently offers a free guided trial using your own data so you can test out how it works for you.

Ease of Use

Tools that are easy to use save on both employee time and training costs. Consider if the platform has an intuitive interface, clear instructions, and helpful onboarding resources. Evaluate how accessible the tool is for non-technical teams and how much training will be required to get everyone up to speed.

Real-Time Analysis

Some organizations need to monitor sentiment as it happens, such as during product launches or PR crises. In this case, you should prioritize tools that offer real-time data processing and customizable alerts.

Consider how you want to be notified about significant changes in sentiment. For example, you might want to set up alerts direct to email or Slack channels so the right teams can get working on the issue immediately.

Customization and Flexibility

Customization ensures you can adapt the tool to your exact requirements. Check if the tool allows you to create custom categories, tweak algorithms, or apply industry-specific models.

Multi-Channel Integration

You need all of your data in one place so you can analyze sentiment effectively. Ensure your analytics tool can easily integrate with your existing CRM, social media management tools, and any other important platforms. Most popular tools offer built-in integrations to all the major platforms.

Multi-Language Support

Global businesses require robust language support. Think about if you need a tool capable of multilingual sentiment analysis across different languages and dialects.

Pricing

Pricing is often the biggest factor when it comes to selecting the right tool. The best solution is one that fits your budget without sacrificing essential features.

Best Practices for Effective AI Sentiment Analysis

In the final section of this article we’ll cover our top recommendations for getting the most out of AI-powered sentiment analysis.

1. Regular Data Updates

If you’re using an AI analytics tool like Thematic you can take advantage of built-in integrations or custom integrations that pull new data for you automatically. This ensures that any new trends should be picked up as they emerge. If you’re choosing to upload your data manually you will need to update it regularly to track any emerging issues.

2. Test for Accuracy and Bias

AI models can make mistakes. They can also be impacted by biases present in the data. This can lead to misinterpretations and inaccuracies. It’s a good idea 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.

3. Industry-Specific Fine Tuning

Different industries and organizations use different types of language and expressions of sentiment. For example, the word “critical” has a different meaning in a healthcare context compared to a customer service department. You could improve accuracy by customizing the model’s training on data specific to your industry rather than using generic models.

4. Set Up Continuous Improvement

When businesses regularly gather and review sentiment analysis feedback, they can keep tweaking and improving their products and services to better meet customer expectations. This feedback loop leads to happier customers and stronger brand loyalty over time.

Conclusion

Most business leaders agree that sentiment analysis should be a priority, yet few businesses actually have a sentiment analysis system. This presents a unique opportunity for early adopters who can leverage sentiment insights to give their customers exactly what they want. Organizations that embrace this technology can expect happier customers, better products and services, and ultimately greater competitiveness and profitability.

Try Thematic on Your Data

Ready to try AI-powered sentiment analysis on your own data? Why not start right here with a free guided demo of Thematic using your data.