Sentiment Analysis: What It Is and How It Works (2026)

Sentiment analysis takes what customers are saying about your business and shows whether they feel positively, negatively, or neutral about your brand. It helps you understand their expectations, what they love, and their reasons for frequenting your business.

More businesses are tapping into the value of sentiment analysis, especially in retail. The global sentiment analytics market was valued at $4.68 billion in 2024 and is expected to reach $17.93 billion in 2034, according to Polaris Market Research. The retail sector accounted for the largest revenue share in 2024.

Here’s how sentiment analysis works, plus how to use it to understand your customers’ needs and expectations and improve business performance.

What is sentiment analysis?

Sentiment analysis, or opinion mining, analyzes qualitative customer feedback, often written or spoken language, to determine whether it contains positive, negative, or neutral emotions about a given subject.

Sentiment analysis involves gathering data from your company’s solicited and unsolicited feedback channels, including surveys, feedback forms, social media comments, emails, chatbot conversations, audio recordings, and video reviews or testimonials.

You then use sentiment analysis tools to determine, for example, how customers feel about your products or services, customer service, and advertisements.

How does sentiment analysis work?

Sentiment analysis typically uses natural language processing (NLP) and machine learning software to analyze and interpret the emotions and opinions expressed in feedback and assign sentiment scores. Sentiments are then aggregated to determine the overall sentiment of a brand, product, or marketing campaign.

Here’s how the process breaks down:

• Collect the data. Pull feedback from every channel, including reviews, surveys, social comments, chatbot transcripts, call recordings, and video testimonials.
• Break down the text. NLP splits each piece of feedback into smaller units of words, phrases, or sentences, and identifies parts of speech, negations, and context.
• Convert words to numbers. The model turns each unit of text into a number it can process. This step, called word embedding, can give similar words similar numbers, so “great” and “excellent” end up close together, while “great” and “terrible” end up far apart.
• Score the sentiment. The model can rate words or phrases on a scale from -1 to 1, where negative feedback scores closer to -1, positive feedback scores closer to 1, and neutral feedback scores near 0. “The fabric feels cheap,” for example, may score closer to -1. “Exceeded my expectations” may score closer to 1.
• Aggregate the results. All those individual scores get combined into one overall score for a brand, product, or campaign. You can then track that score to see whether customers are feeling more positive or negative over time.

That last step is where sentiment analysis pays off, as spotting a pattern in aggregated feedback can point directly to a product fix. Coop Sleep Goods found exactly that in its reviews. 

“We started seeing reviews, people saying, ‘It’s too high. It’s too low,’ says Cofounder Jin Chon in a Shopify Masters interview. “We’re like, ‘Well, we’ll just make it adjustable. Why can’t we do that?’”

Sentiment analysis methods and approaches

There are four main ways to perform sentiment analysis:

• Rule-based
• Machine learning
• Hybrid
• Generative AI and large language models

1. Rule-based

Best for: Store owners with a small, predictable set of keywords to track and no budget for a dedicated tool.

Rule-based sentiment analysis uses NLP techniques to develop a set of rules to determine sentiment. For example, a marketer might create a rule, “Comments that include ‘disappointed’ are classified as negative sentiment.”

Rules are established on a comment level with individual words given a positive or negative score. If the sum of positive scores exceeds that of negative scores, the sample might show positive sentiment, and vice versa. If there’s a tie or the sum is near the middle of the range, the sample may express a neutral sentiment.

Rule-based systems are simple and easy to program but require fine-tuning and maintenance. They also don’t consider context, which can cause analysis errors. For example, sarcasm can mistakenly be classified as positive when it's in fact negative in context.

2. Machine learning

Best for: Store owners who get enough feedback that rule-based keyword matching starts missing context, sarcasm, and nuance.

A machine learning sentiment analysis system uses more robust data models to analyze text and return a positive, negative, or neutral sentiment. Instead of prescriptive, marketer-assigned rules about which words are positive or negative, machine learning applies NLP technology to infer whether a comment is positive or negative.

For example, take, “You’ll never be disappointed by ordering this product!” A rule-based system that labels “disappointed” as negative may classify this as a negative comment, whereas a machine learning approach might infer that it’s a positive comment.

There are several NLP algorithms used for sentiment analysis. Naive Bayes and support vector machines (SVMs) are common choices for straightforward classification tasks, while models like BERT and GPT use deep learning to understand context, tone, and even sarcasm. If your company doesn’t have the budget or team to set up your own sentiment analysis solution, third-party tools like Siena Insights have pre-trained models you can tweak to match your data.

3. Hybrid

Best for: Store owners with brand- or industry-specific language (e.g., slang, product jargon, or niche terminology) that generic models may misread.

Hybrid approaches combine rule-based and machine-learning techniques and can result in more accurate sentiment analysis. For example, a brand could train an algorithm on a set of rules and customer reviews, updating the algorithm until it catches nuances specific to the brand or industry.

Continuous updates make sure the hybrid model improves over time, which improves its ability to accurately reflect customer opinions.

4. Generative AI and large language models

Best for: Store owners who summarize reviews before running them through an LLM, as raw, unedited reviews can give less reliable results.

Generative AI models like ChatGPT and other large language models (LLMs) analyze sentiment by reading text in full rather than breaking it into isolated words or rule-based triggers. Instead of scoring individual terms, the model can weigh tone, phrasing, and sarcasm together to produce a single sentiment judgment for a piece of text.

For example, take a review like, “The room was fine, I guess, if you enjoy paying premium rates for mediocre service.” A rule-based or traditional machine learning model might flag “fine” and “premium” as positive signals, while an LLM may catch the sarcasm and score the review as negative.

A 2025 study of more than 20,000 hotel reviews found that ChatGPT effectively predicted sales when the reviews were summarized by the AI first. On raw, unprocessed reviews, however, ChatGPT performed worse than traditional methods.

Types of sentiment analysis

Polarity classification, which is sorting feedback as positive, negative, or neutral, is among the most basic functions in sentiment analysis. But that’s just the starting point. Beyond polarity, there are four main types of sentiment analysis that each dig into feedback a different way:

• Fine-grained
• Aspect-based
• Emotion-based
• Intent-based

Fine-grained

Fine-grained sentiment analysis uses a rating scale to categorize samples into levels of emotion. For example, when analyzing reviews and ratings, a 1–5 rating scale could translate to: very positive, positive, neutral, negative, and very negative.

Aspect-based

Aspect-based sentiment analysis (ABSA) breaks down text according to individual aspects, features, or entities mentioned rather than giving the whole text a sentiment score. For example, in the review “The lipstick didn’t match the color online,” an ABSA model would identify a negative sentiment about the color of the product specifically.

Emotion-based

Emotion-based sentiment analysis, also called emotion detection, goes beyond positive or negative emotions, interpreting emotions like anger, joy, and sadness. Machine and deep learning algorithms usually use lexicons (a list of words or phrases) to detect emotions. However, this can sometimes result in errors, as some words with a negative connotation can be used in a positive context, like, “The print on the sweater is sick.”

Intent-based

Intent-based sentiment analysis takes into account a text’s sentiment as well as the underlying purpose, goal, and motivation. For example, for the review, “The service was slow, and the food was cold,” the intent would be criticism or a complaint. 

How to use sentiment analysis

Sentiment analysis can show you what customers like, dislike, and expect from your brand based on the words they use in reviews, social posts, and support conversations. Instead of guessing at what’s working, you get a direct read on customer opinion drawn straight from customers themselves.

You can use sentiment analysis in various ways to cater to customer wants and needs:

• Social media sentiment monitoring
• Brand monitoring and sentiment tracking
• Improved customer experience and customer research
• Market research

Social media sentiment monitoring

Use a social listening tool to monitor social media and get an overall picture of your customers’ feelings about your brand, certain topics, and products. 

Social listening has become part of the standard marketing toolkit. In Meltwater’s 2025 Global State of Social Media Report, 75.9% of marketing professionals said they were using or planning to use social listening software. You can even monitor how users feel about your closest competitors. Identify urgent problems before they become PR disasters, like outrage from customers if features are deprecated, or their excitement for a new product launch or marketing campaign.

Because different audiences use different channels, conduct social media monitoring for each channel to drill down into each audience’s sentiment. Platform use varies widely by age. A 2025 Pew Research Center survey found 84% of US adults used YouTube and 71% used Facebook, while half used Instagram. However, Instagram usage among those aged 18 to 29 was 80%, and only 19% among those 65 and older. 

For example, your audience on Instagram might include primarily customers, while your audience on LinkedIn might be just your staff. These audiences are vastly different and may have different sentiments about your company.

“I get alerts on Google from when people mention Unbound Merino anywhere,” says Dan Demsky, CEO of Unbound Merino. “I’ve noticed in the past couple of years, the frequency of Reddit conversations about our brand are way up and they’re all women's clothing."

Brand monitoring and sentiment tracking

Social media isn’t the only place people talk about your company. Take into account news articles, media, blogs, online reviews sites, forums, and any other place where people might be talking about your brand. This kind of brand sentiment analysis helps you understand how customers, stakeholders, and the public perceive your brand, and can help you identify trends, monitor competitors, and track brand reputation over time.

Start by using a sentiment analysis tool to track mentions across various channels, including brand name, product name, hashtags, and keywords related to your brand. Analyze the sentiment behind conversations and understand positive and negative feelings and opinions, including areas of improvement and potential issues. (Remember, more mentions don’t always equal more positive feelings.)

Use sentiment tracking to benchmark performance against competitors over time and identify emerging threats.

Improved customer experience and customer service

Good customer service positively affects your customers and team members. The feedback can inform your approach, and the motivation and positive reinforcement from a great customer interaction can be just what a support agent needs to boost morale.

Sentiment analysis can improve the efficiency and effectiveness of support centers by analyzing the sentiment of support tickets as they come in. You can route tickets about negative sentiments to a relevant team member for more immediate, in-depth help. 

Market research

Sentiment analysis can help you explore new markets, conduct competitive analyses, and identify future trends and opportunities. 

“I think it’s probably the cheapest and most scalable way of including customer feedback as part of an R&D process. They are your customers. They already like the brand. They already like you. They want your product and they want to help you make it better and they’re using it and testing it daily,” says Jin Chon, cofounder of Coop Sleep Goods. “To this day, we read customer reviews. To this day, we bring things up in our product meetings where we’re like, ‘Hey, people are saying that there’s this issue, and if we don’t address it and fix it, somebody else will probably come down the line and do it.’”

Some ways to incorporate sentiment analysis into market research include:

• Product feedback analysis. Understand the sentiment associated with a particular product or its features and functionalities to uncover recurring themes.
• Advertising and campaign evaluation. Use sentiment analysis to identify whether your customers love or hate your latest campaign.
• Market trend identification. Uncover consumer sentiment toward new products, innovations, and industry developments.
• Competitive analysis. Benchmark sentiment scores against your competitors, and identify strengths and weaknesses to capitalize on opportunities.

Sentiment analysis tools

Once you know what sentiment analysis model fits your needs, the next step is choosing a tool. Picking the right one depends on what data you’re analyzing and how deep you need the insights to go. Here are four common sentiment analysis tools:

• Review apps. These collect and display product reviews and star ratings on your storefront. Some include basic sentiment tagging alongside the review itself. Judge.me is one example that offers unlimited review collection with photo and video uploads.
• Review sentiment apps. These go a step further than standard review apps, analyzing the language in each review to surface sentiment trends across your whole catalog, not just star ratings. Yotpo includes sentiment analysis as part of its review analytics, helping you spot patterns and uncover insights so you can identify trends and risk factors.
• AI customer feedback platforms. These analyze sentiment across support conversations, tickets, and live chat, rather than reviews. Gorgias, for example, detects sentiment in incoming customer messages so support teams can flag and prioritize negative interactions before they escalate.
• Social listening platforms. These track sentiment across social media, news, and other public channels outside your store. Sauce, for example, includes social listening alongside its UGC and shoppable video tools on its high-tier plan.

The next question you might ask is should you build or buy your sentiment analysis tool? For many small and mid-sized stores, buying makes more sense. Building your own model can require developers, a data science team, a library of your own data, and ongoing upkeep, which are resources your store may not have. Buying gets you a ready-to-use tool that’s quick to set up and easy to maintain. The trade-off is it won’t always catch niche slang or industry-specific terms as well as a model built just for your store would.

Limitations of sentiment analysis

Sentiment analysis tools are useful, but they’re not perfect. Understanding where they tend to struggle helps you interpret results with a more critical eye rather than treating every score as gospel.

Sarcasm and irony

Sentiment tools may take sarcastic comments at face value. A review like “Great, another product that broke after one use” uses positive words but actually expresses frustration. To catch this, have someone manually check reviews with very high or very low scores, especially when the wording feels a little too casual for how positive or negative the rating is.

Context and negation

Words like “not,” “isn’t,” or “hardly” can flip a sentence’s meaning, which simpler models sometimes miss. “The service wasn’t slow” can get scored as negative because of “slow” alone. Choose a tool built on more advanced NLP or machine learning models, as these can read full sentences rather than scoring word by word.

Mixed sentiment

A single review can express more than one opinion. “The dress fit perfectly but arrived two weeks late” is both positive and negative. Look for a tool that supports aspect-based sentiment analysis so it can score “fit” and “delivery” separately instead of giving the whole sentence one score.

Slang, jargon, and cultural nuance

Words carry different meanings across regions, generations, and industries. “Sick” or “fire” mean something positive in casual slang but negative in a literal sense. Update your sentiment tool’s lexicon or training data regularly, and if you sell internationally, use a tool that supports region-specific language models rather than a single global one.

Sentiment analysis tools are getting better all the time, but there isn’t a tool that catches everything. So, treat sentiment scores as a starting point and spot-check results against a sample of real customer feedback to see what the tool misses. 

Even positive-sounding feedback can be misleading if you take it too literally, as Issues Magazine Shop found when customer requests didn’t translate into sales. 

“Just because someone wanted us to carry something didn’t mean they were going to come in and buy it,” says Nicola Hamilton, founder of Issues Magazine Shop, in a Shopify Masters interview. “It didn’t mean they were going to part with their hard-earned dollars to purchase that thing, and so we definitely sort of swung the pendulum too far toward what the customers were asking for a while.”