Most LLM Sentiment Tracking Is Misguided

If you’re tracking the sentiment of all LLM outputs in your AI monitoring tool, you’re probably measuring the wrong thing.

Sentiment is an illusion. LLMs like ChatGPT, Claude, or Gemini aren’t forming opinions. They’re not thinking creatively or offering perspective. They’re trained to generate human-sounding responses based on massive datasets of human language: forums, blogs, product reviews, Reddit threads, news articles, and more.

So what about using LLMs as an aggregator?

They can synthesize public opinion across thousands of sources to give some high-level understanding.

If I were to ask: “What do people on Reddit say about [Product]?”

The model’s answer reflects real human sentiment patterns, even if it’s an echo. And I’ll agree that there’s still some value there, but most people are not only tracking “what do people say about [thing]” type questions in their AI monitoring tool.

Why Most People Get This Wrong

Here’s where I have a problem with sentiment analysis on LLM answers – a lot of folks doing LLM tracking are asking questions like:

• “What are the best CRMs?”
• “Who are the top email marketing platforms?”
• “What are the best solutions for [industry problem]?”

Then they analyze the sentiment of the LLM’s output – trying to understand how these models “feel” about their brand.

The problem? Those types of solution-seeking queries don’t have a sentiment. They’re about brand visibility and recommendation ranking, not emotion. The LLM isn’t choosing based on preference – it’s predicting what someone might say based on the most common patterns in its training data.

Just because a brand shows up in a list doesn’t mean the model is endorsing it. Visibility ≠ favorability.

But the LLM is likely talking about its own recommendations favoribly, so the sentiment will be positive. It’s a self-fulfilling prophecy.

Framing Matters (A Lot)

Let’s say someone does ask the model about a specific company:

“Tell me about [Company X]”

That’s probably the best scenario where sentiment tracking could be useful – especially if you’re analyzing a large number of company-specific prompts using a consistent input format.

But here’s the problem: people often introduce framing bias without realizing it. They ask:

“What are the positive things about [Company X]?”

“What are the negative things about [Company X]?”

You’ve introduced bias. You told the model what tone to take. Any sentiment analysis that follows is just a reflection of your prompt – not a signal about how the world sees the brand.

When teams take this kind of sentiment analysis at face value, they risk building messaging, positioning, or even campaigns based on outputs that were biased by the prompt structure itself.

Sentiment Analysis ≠ Insight

This is where most LLM sentiment tracking doesn’t make sense. You’re measuring what the model thinks someone might say in a certain context based on the tone you prompted.

There are use cases where sentiment tracking might help:

• High-level queries like “Tell me about [Company]” with consistent, neutral prompts
• Controlled benchmarking using prompt ensembles or fine-tuned models
• Tracking LLM bias itself, as a reflection of training data or model behavior

But if you’re trying to analyze LLM outputs to figure out the sentiment of  who ranks higher in a top-10 list then you’re asking the wrong questions.

So What Should You Actually Do?

If you’re tracking sentiment in LLM outputs, here’s how to make sure it’s valuable:

1. Be intentional with your prompts

If you’re going to measure sentiment, make sure you’re asking open-ended, neutral questions. No leading language. No built-in tone. Avoid prompt phrasing like “What are the pros and cons of…” or “What’s great about…” unless you want the model to take sides (because it will).

If you’re also tracking other types of prompts – like brand visibility or solution recommendations – keep them separate. Don’t treat all LLM outputs as equal sources of sentiment. Recommendation prompts and sentiment prompts are for different purposes – don’t try to mix them up.

2. Go to the source

Want real sentiment? Try going where people are actually talking.

Look at Reddit threads. Read the reviews. Dig into testimonials and blog comments. LLMs can be a helpful tool to summarize or categorize that human-generated content, but don’t treat them like a shortcut to human opinion.

If your goal is to understand what people actually think – try starting with people. LLMs can help you scale your insight, but they shouldn’t be your source of truth.

Understanding where to go and what to measure in digital marketing + LLMs is hard. Anyone who says they have the answer is pulling your leg. Want to start reimagining how your strategy is built and measured based on your customer data? Let’s chat.