Why Sentiment Matters More Than Ever

What your reviews are really saying (and how to use them to your advantage).

You can’t talk about local visibility without talking about how people feel.

When you’re checking out a business, you don’t just look at stars. You likely read the reviews as well to see exactly what the common issues are or if the business is trustworthy/legit. It’s not just what people say, it’s how they say it. That tone sticks. It shapes trust, conversions, rankings, and even how LLMs can summarize your brand.

If you’re only looking at reviews to see that a business is getting them and that they are positive, you’re missing what they can actually tell you.

I’ll break down why sentiment matters more than ever, how to pull those insights, and how to turn them into something useful.

Local SEO

Reviews build credibility and influence rankings. Google’s local algorithm uses review signals (quantity, freshness, keywords) to help determine relevance and prominence. A business with consistently positive, detailed reviews stands a much better chance of outranking competitors with the same star rating.

LLMs

When someone asks an LLM about your brand, the model generates a response based on the language it’s seen across the web. That includes websites, news articles, forums, and potentially user-generated content like reviews if that language shows up in public data sources.

Additionally, modern LLMs that use Retrieval-Augmented Generation (RAG) can access real-time information from the internet, going beyond their static training data to provide current information. These reviews shape your broader online narrative, and patterns in sentiment can influence how your brand is interpreted and described in AI-generated responses.

Studies from 2024 demonstrate that LLMs significantly outperform traditional sentiment analysis techniques in identifying and categorizing sentiments from customer feedback. If your reviews consistently highlight things like “slow service” or “unreliable hours,” that kind of language can feed into how a model perceives trustworthiness, as LLMs analyze context and nuances to derive meaning from sentiment patterns. On the flip side, recurring mentions of “friendly staff” or “quick response time” reinforce reliability and positive brand attributes.

Mordy Oberstein shared an example of sentiment analysis while analyzing DraftKings vs. FanDuel using Semrush’s AI Analytics tool. The tool tracks brand representation across LLMs, and FanDuel came out ahead in both appearance frequency and sentiment.

From there, he used AlsoAsked to get context around the reason for the sentiment analysis scores. The general sentiment of the questions about FanDuel was neutral, like “Can you cash out free bets?” These reflect expected consumer questions. DraftKings, on the other hand, had questions with negative sentiment, like:

“Does DraftKings actually pay out?”

“Why does DraftKings need my SSN?”

“Should I trust DraftKings?”

These questions carry an implied skepticism that reflects real trust barriers. And that tone likely stems from repeated negative signals in user language, possibly including reviews, public Q&A content, and social commentary.

While we can’t control every mention, we can address concerns from reviews and try to get ahead of some issues.

How to learn from reviews

Most people think about reviews in terms of reputation management, responding to the negative ones and collecting the positive ones. But if that’s all you’re doing, you’re leaving a lot on the table.

Reviews can tell you:

• What customers actually care about

• What’s working (or not) in your service or experience

• How you stack up against the competition

• The exact language people use to describe your brand

This is useful for more than just customer support. It’s insight you can use in SEO, CRO, content, and brand messaging.

That’s why I built the GBP Reviews Sentiment Analyzer Chrome extension to make this kind of analysis faster and actually usable.

GBP Reviews Sentiment Analyzer

Here’s how it works (and here is a video if you want to watch it in action):

1. Open a business’s Google listing (I tested on Porto’s Bakery, which has 18k+ reviews)

2. Sort reviews by newest (it won’t run unless sorted)

3. Click “Analyze Reviews”

You’ll get:

• A sentiment breakdown of the most recent 10 reviews

• Top recurring nouns and adjectives

• Review-level insights (who said what, and how they felt about it)

• Export reviews from up to a month ago as a CSV to sort, filter, or drop into Sheets for client reporting or deeper analysis

This gives you quick insights into the emotional tone behind your reviews. If you try the extension, I’d love your feedback. Feel free to reply and let me know how you’re using it (or what you wish it did better!)

What to Do With the Insights You Pull

Once you’ve analyzed and exported your reviews, here’s how to put that data to work:

1. Address repeat issues

If certain complaints show up often, address and clarify them on your site. Add FAQs, update service blurbs, and flag UX problems worth testing.

2. Use winning language

Pull standout phrases from positive reviews and work them into your H1s, SEO titles, alt text, and internal links.

3. Spot themes by location

For multi-location businesses, review tone and issues often vary by area. Segment your insights to localize content and prioritize updates.

4. Monitor competitor reviews

See what customers think about your competitors. Address their shortcomings on your website. If customers consistently complain that it takes too long to schedule an appointment with a competitor, be sure to mention that you offer same-day appointments.