Customer reviews are often the first place product and CX teams go when something feels off—but they’re rarely treated as a reliable source of strategic insight. They’re scattered across platforms, inconsistent in tone, and filled with noise. Parsing them at scale feels like more trouble than it’s worth.

But when done right, review data can reveal exactly where customers are getting stuck, what features are falling short, and what moments genuinely delight—across software, apps, physical products, and services.

This article walks you through sentiment analysis using product review data to uncover what users love, what frustrates them, and what needs to change. With the right approach, sentiment analysis can help you transform noisy, unstructured reviews into clear priorities, without needing a data science team or months of manual tagging.

1. Collecting Product Review Data

Review data comes in many forms: SaaS feedback on G2, app store ratings and comments, public reviews of physical goods on Amazon, and even feedback on services from platforms like Trustpilot or Yelp.

Some teams also use internal sources, like NPS survey responses or in-app feedback tools, as part of their review ecosystem. The volume and type vary, but the opportunity is the same: mine unsolicited, high-signal input from real users to understand what’s working and what’s not.

Here’s how to start collecting this kind of data across different platforms:

G2 and Capterra

If you’re in B2B or SaaS, G2 and Capterra reviews are gold. They come from experienced users and often include detailed comments on UX, onboarding, integrations, and support.

G2 makes it easy to download reviews as CSVs or via API. Just watch for pagination limits and rate caps.

Capterra doesn’t offer a public API, but its reviews are accessible through exports and scraping (if done ethically).

App Store and Google Play

For teams working on mobile products, the App Store and Google Play offer a rich stream of user sentiment tied to version changes, bugs, and new features. Apple and Google both allow developers to access review data via official APIs. These platforms are especially valuable for tracking the impact of releases and catching recurring issues early.

Amazon and Retail Platforms

Amazon remains one of the largest sources of consumer review data, especially for physical products. While it doesn’t offer a clean public API, sellers can download their own reviews, and researchers can tap into existing datasets or use compliant scraping methods.

Just be mindful: Amazon removed over a billion fake reviews last year, so filtering noise is critical.

Trustpilot and Yelp

Trustpilot is common for service-based businesses, while Yelp is essential for local service providers like restaurants, salons, or health clinics. Both offer public-facing data, but only Trustpilot provides a proper business API.

When analyzing these platforms, make sure to anonymize user data and focus on patterns rather than isolated complaints.

2. Cleaning and Preprocessing Review Data

Once you’ve got the reviews, the next challenge is making sense of them. And that starts with cleaning things up. Raw review data is noisy. If you want accurate sentiment analysis, you need to sort signal from noise.

Here’s how to get your review data in shape:

Filter Out Fake and Spammy Reviews

Not all reviews are real or helpful. Some are auto-generated, copied, or just plain nonsense. If left in, they can distort your sentiment results. Platforms like Amazon spend hundreds of millions fighting review fraud, and for good reason: about 30% of reviews online are fake.

Use simple rules or trained models to flag suspicious patterns, like identical phrasing, gibberish, or generic praise with referral links. Then down-weight or exclude them from your analysis.

Deduplicate and Consolidate

If you’re pulling data from multiple platforms, duplicates are inevitable. Same review posted to two sites? Same customer updating their review twice?

Keep the version that matters most—usually the most recent—and ensure each opinion only counts once.

Normalize Emojis and Slang

Reviews today come with all the feels—literally. Emojis like 😠 or ❤️carry sentiment and shouldn’t be ignored.

Convert them into text equivalents (“angry face,” “heart”) so your sentiment model can read them properly. The same goes for slang and shorthand. "IMO," "lol," and "ugh" all tell you something—don’t let them get lost in translation.

Handle Multiple Languages

If your customers speak more than one language, your reviews will too.

Step one: detect the language.

Step two: decide what to do—either translate them (using reliable APIs) or use multilingual sentiment models that can process them as-is.

Just don’t skip them. Some of your most passionate feedback might come in French, Spanish, or German.

Use Star Ratings and Metadata

If your reviews include star ratings, timestamps, or product IDs, use them.

A 1-star review is likely negative, but not always.

Use these ratings to double-check your sentiment output. You can also track trends over time (are reviews getting better or worse?) or compare sentiment across product versions, launches, or user types.

3. Choosing a Sentiment Analysis Approach

Once your data’s cleaned up, it’s time to figure out how you’ll actually analyze the sentiment behind those reviews. You don’t have to do this manually; automated sentiment analysis will make this much easier.

And you’ve got two main routes: tap into the power of large language models (LLMs) like GPT-4, or go the more traditional route with a custom-trained machine learning model.

Each has its strengths—and trade-offs.

Option 1: Use a Large Language Model (LLM)

With an LLM like GPT-4, you can throw in a review and ask: “Is this positive, negative, or neutral?” No training required. These models have read the internet and can often detect tone, sarcasm, and nuance. Say someone writes: “Great. Just great… 🙄”—GPT-4 will usually get that it’s not actually great.

LLMs are great for teams that need fast, flexible insights without building a full ML pipeline. They're especially useful when you’re working with short timelines or exploratory research.

The downside? They can be pricey to run at scale, and a bit unpredictable—you’re relying on prompt engineering, not model tuning.

Option 2: Train a Custom Model

Prefer full control and repeatable outputs? You can build your own classifier using a labeled dataset—reviews tagged as positive, negative, or neutral—and train it using traditional ML (SVM, logistic regression, LSTM, or even a fine-tuned BERT). This takes more effort upfront, but once trained, it’s fast and cheap to run, especially if you’re processing large volumes.

Custom models are also easier to validate and audit. You know exactly how they behave on your test data. But they can miss context that an LLM would catch unless trained really well.

Option 3: Combine Both

A lot of teams go hybrid. They’ll use an LLM to get early insights or tag a small sample of reviews. Then they use that labeled data to train a faster, more cost-effective model. Or they’ll use LLMs for the hard stuff—like figuring out sentiment per product feature—and rely on a traditional model for simpler classification.

Option 4: Use a Sentiment Analysis Platform

If you want the benefits of LLMs and custom models—without the technical overhead—consider a sentiment analysis platform built for this purpose. These tools often blend generative AI with specialized models trained on customer feedback, making them ideal for business users.

Thematic is one such platform. It uses Large Language Models (LLMs) to capture nuance and uncover emerging themes in feedback. Its sentiment analysis feature—purpose-built for customer feedback—delivers consistent, structured insights. So you get flexibility, depth, and reliability, all without needing to build or train your own models.

4. Aspect-Based Sentiment: Mapping Feelings to Features

Knowing that 20% of your reviews are negative is useful, but not enough. What really matters is why. What are people upset about? Is it the product itself? The delivery? The customer support?

That’s where aspect-based sentiment analysis comes in. It’s the step where you connect emotions to specific themes—so instead of vague sentiment, you get clear direction.

What’s an Aspect?

Aspects are just the topics customers mention in their reviews.

• For a mobile app, that might be app stability, login speed, or notification timing.
• For a SaaS platform, it could be onboarding flow, feature access, or billing clarity.
• For a physical product, you might still be tracking battery life, build quality, or delivery issues.

These are the things customers are reacting to—and how they feel about them tells you where to focus.

How to Tag Aspects in Your Reviews

There are two main routes:

1. Keyword tagging

The old-school way. You define a list of keywords for each aspect—like “battery,” “charge,” and “power” for battery life—then scan for those terms. It’s simple and fast, but it misses nuance. A phrase like “battery of laughs” might get misclassified, and you won’t catch synonyms you didn’t include.

2. Machine learning or LLMs:

Smarter methods use AI to identify aspects automatically and link them to sentiment. For example, an LLM like GPT-4 can take a review and output something like:

“Battery life – negative, Camera – positive, Shipping – neutral.”

That level of granularity turns messy text into structured feedback.

Once aspects are tagged, match them with the relevant sentiment. A review like “The login was smooth, but the app kept crashing” becomes:

• Login experience: Positive
• App stability: Negative

Now you’ve got something your product team can actually work with.

Better yet, visualize the data. Create an aspect sentiment matrix—a simple table showing how many positive vs. negative mentions each feature gets. You might find that:

• App crashes: 50 negative, 5 positive
• Login experience: 35 positive, 10 negative
• Feature discoverability: 25 negative, 20 positive

Now you know what’s dragging down your customer experience—and what’s worth promoting.

5. Prioritizing Product Fixes with an “Action Board”

Once you've mapped sentiment to specific product or service aspects—like onboarding, delivery, or support response time—it's time to turn those insights into action. The goal here is to prioritize fixes or improvements that will have the most significant impact on customer satisfaction.

Creating an "action board"—a straightforward dashboard or table that ranks product features by their impact on sentiment—can guide your team effectively.

Calculate Volume and Sentiment for Each Aspect

From your aspect-based sentiment analysis, note how many total mentions each aspect has and what percentage (or count) of those are negative. For example:

• App crashes – 100 mentions, 60% negative
• Packaging damage – 80 mentions, 5% negative
• Onboarding experience – 75 mentions, 35% negative

Compute an Impact Score

Not all negative feedback is created equal. A minor issue mentioned in a few reviews is less urgent than a major problem cited in hundreds.

A simple yet effective metric is Volume × Negativity.

You might also factor in the average star rating of reviews that mention that aspect. For instance, if all battery-related complaints are in 1-star reviews, that's a critical issue.

Rank the Aspects

Sort features by their impact score or the sheer count of negative mentions. This ranked list becomes your pain-point leaderboard, highlighting which problems, if addressed, could significantly boost overall sentiment and metrics like NPS.

Add Business Context

Some aspects might generate noise but little impact. For example,50 complaints about icon styling may not move the needle, but 50 complaints about failed logins or subscription errors could mean churn is on the rise.

Incorporate frameworks like RICE (Reach, Impact, Confidence, Effort) to prioritize effectively.

Visualize the Action Board

For each top issue, assign an action or owner. For example:

• App crashes – Engineering to investigate crash logs
• Pricing confusion – Product team to review billing UI and help content
• Delivery delays – Ops team to review fulfillment partners
• Account lockouts – CX to analyze login support patterns
• Integration delays – DevOps to streamline API setup

This board bridges customer feedback and your product backlog, ensuring that the Voice of the Customer drives improvements systematically. Instead of reacting ad-hoc to the loudest complaints, you're using data to focus on what will move the needle for most customers.

Lastly, communicate this internally. An action board derived from real customer reviews can be a powerful tool to show executives or other teams why certain fixes matter. It quantifies customer pain and provides a clear roadmap for enhancements.

Atom Bank used Thematic to surface the most common friction points in their mobile app reviews—and link those directly to dips in customer satisfaction scores. The insights helped them prioritize UX fixes that moved the needle fast. Read the full case study.

6. Real-Time Alerts and Automation

Sentiment analysis of reviews doesn’t have to be a once-a-quarter dashboard check. When done right, it becomes your early warning system—alerting you to brewing issues or breakout wins in near real time.

Imagine knowing on day two that a new app update is crashing for some users, because the last 50 reviews dipped sharply in sentiment. Or spotting a pattern of praise around a new feature, so your marketing team can double down while it’s hot. That’s the power of automation.

Here’s how to make it happen:

Set Up Sentiment Alerts

With tools like Thematic Workflows, you can track the rolling average sentiment of new reviews. If it drops suddenly—say, by more than 0.15 standard deviations—trigger an alert. Maybe that’s a Slack ping to your product team, or a Jira ticket with a pre-filled priority tag. You don’t need to chase down issues manually. They come to you.

Use AI Summaries for Digestible Insights

Platforms like Amazon now auto-summarize product reviews for shoppers. You can borrow that trick. Use LLMs or summarization models to create a weekly internal digest: “Top themes: fast shipping, poor battery life.” These mini-reports help teams stay on top of shifting sentiment without sifting through thousands of comments.

Push Insights Where Teams Already Work

Integrate your sentiment monitoring with tools your teams already use—Zendesk, Jira, Slack. For example, if a review mentions “refund” and has a very negative tone, automatically generate a support ticket. Or send your exec team a Friday recap: “This week: 200 new reviews, 78% positive. Top complaint: delivery delays.”

Monitor Competitor Reviews, Too

Don’t just watch your own sentiment. Keep an eye on competitors. If their new product is getting slammed for a feature you also offer, it could be a warning sign—or a marketing edge. Set up keyword tracking to monitor public mentions and reviews.

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Turn Reviews Into Action—Faster Than You Think

Product reviews are one of the richest sources of insight most teams overlook. Whether you’re building a mobile app, scaling a SaaS platform, or delivering physical products, reviews capture what users care about most—in their own words.

With the right structure, you can clean the noise, tag feedback to the right teams, and prioritize what to fix next, without getting buried in dashboards or transcripts. This playbook showed how sentiment analysis using product review data can bridge the gap between scattered feedback and focused action.

Curious what your customers are already telling you? Try Thematic on your own data and start turning feedback into decisions you can stand behind.