When was the last time you truly understood how your customers feel?

If you’re in customer service, you know it is not how much feedback you get that matters. It's understanding the sentiment expressed by customers that counts. That makes sentiment analysis key to improving experiences, refining strategies, and driving growth.

But imagine you're getting hundreds, if not thousands, of pieces of feedback. Would you dare analyze sentiment on your own? That would take ages to complete and risk inconsistencies.

Hence, the a need for automated sentiment analysis. With artificial intelligence (AI), you can quickly and accurately analyze customer feedback at scale.

Here's what the numbers say: 70% of customers see a clear difference between companies that use AI well and those that don’t. Businesses that lead in AI are also much more likely to see big returns from their efforts—over 100% more than those that don’t.

So, in this blog, let's talk about automation with sentiment analysis tools. We'll cover:

• how it works,
• why it's important, and
• how you can get started with it.

Let's start.

Why Automate Sentiment Analysis?

Let’s say you’re managing customer feedback, survey responses, and even news articles for a growing business. You want to see the overall sentiment about your service so you can decide your next actions to improve the customer experience.  So you dive into your social media platforms, hoping to do a customer review analysis, but there’s just too much text data to read!

Even if you do have enough time to do sentiment analysis yourself, be it manually or using spreadsheets, there are just too many issues in this scenario:

• It's overwhelming; you might not be able to keep up when feedback floods in.
• It's prone to inconsistencies and errors; people may interpret data differently.
• More importantly, you're using too much precious time that you could allocate to high-level strategizing.

Unfortunately, today's fast-paced market has no room for slow and inaccurate processes. This is a recipe for losing customers.

Enter automated sentiment analysis model. Here, artificial intelligence does the heavy lifting for you. AI will extract valuable insights from a large volume of textual data and identify trends using consistent criteria. This sentiment analysis process gives you a better understanding of what customers feel.

Incorporating AI in the sentiment analysis model gives you:

• Efficiency: AI analyzes particular aspects of thousands of customer comments in seconds, not days.
• Scalability: Whether you’re handling 10 or 10,000 responses, automation doesn’t slow down.
• Accuracy: AI is consistent—it doesn’t get tired or make emotional judgments, so you get accurate insights.

In a Harvard Business Review, Brinks Home, a smart-home company, explained how it used AI to analyze customer feedback. By automating its processes using sentiment analysis tools, it could personalize every touchpoint. It then saw direct-to-consumer revenue rise by 9.5% in just two years.

Clearly, applying sentiment analysis with automation saves you time. The best sentiment analysis tools give you a consistent, accurate analysis of how customers truly feel. That way, you can make and keep your customers happy.

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Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) takes sentiment analysis a step further by identifying not just how customers feel but "what they feel about."

For example, a review like “The app is easy to use, but it crashes often” links positive sentiment to “ease of use” and negative sentiment to “crashing.”

Aspect-based sentiment analysis helps businesses pinpoint what’s working and what needs fixing, giving more actionable insights than knowing whether the feedback is positive or negative.

In the next section, we will look deeper into how automating with sentiment analysis tools works.

How Automated Sentiment Analysis Works

You’ve probably used generative AI like ChatGPT or CoPilot at one point and got frustrated when you didn’t get the response you wanted. You were probably so annoyed you wanted to explode. Then you wondered, “Does ChatGPT even have emotion, and will it understand how I feel?”

Aah, that age-old question: what if AI can feel? Well, that's very unlikely to happen any time soon. But can it understand your emotions? Yes, it can. AI can be programmed to understand emotions through data patterns and proper training, making sentiment analysis much easier.

When used in sentiment analysis, artificial intelligence can scour through a sea of unstructured data in the blink of an eye, pinpointing negative sentiment vs. positive sentiment. It takes care of that one tedious part of your qualitative data analysis process so you can move on to other tasks.

Here's a case of how sentiment analysis works.

Picture this: You're doing market research. You noticed you're getting tons of textual data from customer feedback on social media platforms, survey responses, and news articles. You want to understand the overall sentiment of customers so you can help your customer service teams improve customer experience and boost customer satisfaction.

So, you turn to a sentiment analysis tool.

This sentiment analysis software will go through social media posts, for instance, and use thematic analysis or semantic analysis to determine the emotional tone of customer feedback. It looks for positive, negative, or neutral tones from the unstructured data for sentiment classification.

Let me explain further how sentiment analysis tools make the magic happen.

1. The Magic of Natural Language Processing

Sentiment analysis, when automated, will involve natural language processing (NLP).

NLP helps computers make sense of human language. Simply put, it's like teaching a machine to “read” and “understand” how we express ourselves in text. But more than just reading, it can figure out the meaning and emotion behind words. It doesn't matter the language, too, with multilingual sentiment analysis tools.

Let's say a customer left a review: “The service was outstanding! ”

A basic system might only see individual words, but natural language processing sees the whole statement.

So, it knows that "outstanding" is a positive word and that it describes "service" in this context. Using rules, dictionaries of emotional words, and advanced models trained on millions of similar sentences; natural language processing understands that this is positive feedback.

However, in semantic analysis, natural language processing (NLP) will also look at words surrounding "outstanding" and check for any words that might change their meaning. For example:

“The service was outstanding! ” → Positive sentiment.

“The service was outstandingly bad.” → Negative sentiment.

So, by analyzing words, grammar, and overall context, natural language processing (NLP) in sentiment analysis can tell if feedback is positive, negative, or neutral. And this is happening at lightning speed.

2. Learning from Examples with Machine Learning

Found in sentiment analysis API, machine learning (ML) is how computers learn to spot patterns by practicing on real examples. In sentiment analysis, machine learning starts with a set of sentences already tagged as positive, negative, or neutral. For example:

Positive: “I love this product!”

Negative: “This is terrible.”

Neutral: “The item arrived on time.”

Through machine learning, the AI will analyze sentiment in these examples to find patterns, such as positive words (“love”) or negative ones (“terrible”). Over time, it gets smarter, learning to handle tricky cases like sarcasm. For instance:

“Oh great, another delay!”

Even though “great” is a positive word, the context (“another delay”) tells the AI it’s negative.

You know that saying "experience is the best teacher"? That's pretty much how ML sentiment analysis works. With enough examples, a sentiment analysis API becomes better at understanding the tone of new sentences, even when they’re complex.

3. Advanced AI Models in Action

Advanced sentiment analysis tools today use Large Language Models (LLMs)—big, powerful systems trained on tons of text, like ChatGPT. These models can pick up on subtle emotions, like excitement or frustration, making sentiment analysis even more accurate.

For example:

“I’m thrilled with how quickly it arrived!” → Positive.

“This is just plain annoying.” → Negative.

Most sentiment analysis tools, like Thematic, take it further by not only spotting emotions but also identifying the topic behind them.

For instance, if many people complain about “delivery delays” in social media, the sentiment analysis algorithms in the tool link that topic to negative sentiment. With this, businesses get a clear picture of overall sentiment and what needs fixing.

Ultimately, automated sentiment analysis tools can complete in seconds what humans would otherwise take weeks or months to accomplish. You can analyze social media posts, reviews, and survey responses without breaking a sweat. That also means you can act on feedback before it’s too late.

Use Cases for Automated Sentiment Analysis

As we said earlier, automated sentiment analysis helps businesses understand the emotions behind words. Users leverage sentiment analysis tools to determine sentiments through thematic analysis and semantic analysis.

Sentiment analysis tools help with opinion mining for brand sentiment analysis and more. You glean valuable insights from identified emotional tones and sentiment scores that help in market research, improve customer experience, and empower customer support teams.

But if you're still doubtful about the benefits of automation in sentiment analysis, let’s look at some cases.

1. Analyzing Customer Reviews

Customer reviews are a goldmine of insights. But reading through hundreds of them is exhausting, and doing sentiment analysis on them on your own is a headache. That's exactly why Brinks Home decided to use sentiment analysis software. They wanted to sift through vast amounts of customer feedback quickly and accurately.

Brinks Home’s decision to automate sentiment analysis proved successful.

With a sentiment analysis system in place, they identified recurring issues through emotion analysis, such as dissatisfaction with call center interactions or product usability. With such insights, they were able to implement targeted improvements. The results? We said it earlier: revenue growth and higher customer satisfaction.

2. Tracking Social Media Sentiment

Social media is where your reputation could either soar or crumble in real time. Starbucks uses automated sentiment analysis for social media monitoring and opinion mining. With emotion analysis, they can better respond to customer reactions on social media, especially easily addressing negative feedback.

They track social media conversations about seasonal beverages and promotions in particular. In doing so, they are able to tailor messaging and offerings to align with customer preferences. The insights gathered from sentiment analysis help them fine-tune campaigns so they stay relevant and impactful.

3. Measuring Employee Satisfaction

Employee feedback is just as vital as customer input. Just imagine having unhappy workers. That's why DoorDash sought help from Thematic. They wanted to decode feedback from their delivery drivers, or “Dashers.” Thematic performed sentiment analysis, highlighting key themes, such as the importance of flexible working hours.

Acting on these insights from the sentiment analysis, DoorDash introduced a rewards system for top-performing Dashers. Soon after, driver satisfaction and retention improved.

Steps to Start with the Automated Sentiment Analysis Model

Identifying and analyzing the sentiment of specific aspects in a text—aspect-based sentiment analysis—does not have to be tedious. Sentiment analysis software is here to analyze qualitative feedback in a breeze and help you understand what customers feel. You only need to find the best sentiment analysis solution for your business.

Here's a step-by-step guide to get you started with sentiment analysis tools:

1. Define Your Goals

Just like in any other endeavor, you need to be clear about what you want to achieve. But why do you need a sentiment analysis system in the first place?

Ask yourself:

• Are you trying to improve customer experience or satisfaction scores?
• Do you need insights to optimize a product or service?
• Are you monitoring brand reputation across social media platforms?
• Do you want to collect data for competitor analysis of customer sentiment?
• Do you want brand sentiment analysis to improve your reputation?

Defining your objectives will help you select the right sentiment analysis solution and focus your efforts where they matter most.

2. Gather and Prepare Your Data

Once it's clear to you what you want with your sentiment analysis tools, start collecting data from relevant sources:

• Direct feedback: Surveys, reviews, or NPS (Net Promoter Score) responses.
• Indirect feedback: Social media posts, chat transcripts, and support tickets.

Be sure data sets are clean and organized. Thematic can automatically pre-process data, removing irrelevant details like hashtags or formatting inconsistencies​ before running sentiment analysis.

3. Choose the Right Tool

The best sentiment analysis tool will depend on your business size, data complexity, and goals. Here’s what to consider:

• Ease of Use: Sentiment analysis tools should be intuitive, saving time for your team. A user-friendly interface allows even non-technical members to get actionable insights without steep learning curves.
• Scalability: If you’re dealing with high volumes of data, ensure the sentiment analysis tool can handle it. For smaller businesses, a straightforward tool like Zoho Analytics may suffice, while larger organizations need robust capabilities.
• Customization: the best sentiment analysis tools allow you to tailor analyses to your business needs and help ensure that insights are relevant and impactful.

Thematic uses advanced AI for thematic analysis. That means it automatically identifies recurring themes and links them to customer sentiment. This also means you can quickly uncover what matters most to your customers and take action faster.

What sets Thematic apart:

• Advanced Insights: It performs sentiment analysis based on combined theme-combined sentiments, providing a deeper understanding of customer feedback.
• Flexibility: Easily customize themes using the built-in Theme Editor, ensuring analyses align with your business goals.
• Scalability: Thematic adapts effortlessly when performing sentiment analysis in a few hundred reviews or millions of data points.

4. Set Up Integration

Integrate your sentiment analysis tool with existing platforms:

• CRM systems (e.g., Salesforce) to tie sentiment data to customer profiles.
• Social media monitoring tools for real-time sentiment tracking.
• Feedback channels like survey tools or review sites.

Thematic integrates with Qualtrics, Power BI, and a lot more. Multi-channel integration ensures a holistic view of customer sentiment across touchpoints.

5. Analyze and Act

Once your sentiment analysis tool is set up:

• Run the analysis to identify key themes and sentiments.
• Combine sentiment scores with context to prioritize issues. For example, feedback like “delivery delays” with negative sentiment might call for operational improvements​.

Use dashboards to track trends over time, such as improving customer sentiment after a product update.

When running sentiment analysis, Thematic gives you more than just sentiment scores. You get a clear roadmap for action you must take.

6. Continuously Optimize

Customer preferences change, and so will their sentiments. That makes sentiment analysis an ongoing process. So, regularly update your data sources and refine your sentiment analysis based on new patterns or feedback.

Thematic makes regular sentiment analysis easy by helping you customize themes, ensuring insights remain relevant as your business evolves.

Make Customer Feedback Work for You

Sentiment analysis is important in understanding what your customers feel and how to better serve them. But as we've said over and over, sentiment analysis done on your own is not an easy feat.

Sentiment analysis tools are your best bet if you wish to know what customers feel about your brand reputation but have no time to dive into a sea of customer feedback.

The best sentiment analysis tools turn valuable data into valuable insights. It uses natural language processing (NLP), machine learning, or large language models to do its magic.

Tools like Thematic use a sentiment analysis model paired with natural language processing and machine learning to help you find what matters—customers' overall sentiment.

But that’s just the half of it. Isn’t it? Identifying the right courses of action and having all the information readily available to guide you through improving customer satisfaction is just as important. Thematic does that, too.So why wait? Leverage sentiment analysis now, and make feedback count. Get a demo of Thematic today.