A prospect rarely calls you without any prior knowledge. They have already read and heard things, and more and more often they have also asked an AI assistant what to make of your company. If that answer mentions you with a caveat, that caveat comes back as an objection in your first conversation. With sentiment analysis, you see it coming before the conversation takes place.

What is sentiment analysis?

Sentiment analysis is the automatic detection of the tone of a text: whether it is positive, neutral or negative about a subject, such as a brand, a product or a service. The result is usually a label, sometimes with a score between 0 and 1. So sentiment analysis does not measure whether something is talked about, but how, and it is widely used for customer reviews, social media and news articles.

A good analysis looks at the words around the brand name, not at the tone of the whole text. An article can be about a difficult sector and still be full of praise for one supplier. So always read a score together with the text it comes from: it points in a direction, but you make the judgement yourself.

Sentiment in AI answers

When someone asks an AI assistant which supplier fits their situation, they don't get a set of search results, but an answer in full sentences, with names and a verdict. That verdict is what sentiment analysis looks at.

In the GripIQ Intent Engine, every answer gets one of four labels: Positive, Neutral, Negative or Mixed. Mixed means the answer combines praise and criticism, for example "strong on service, weaker on price". Next to the label, you see a sentiment score between 0 and 1 for each answer. A score of 0.6 or higher counts as positive, from 0.4 as neutral, and anything below that as negative.

You will find sentiment in several places:

  • In AI Visibility, the Avg. sentiment card shows the average score of your own brand, and the Rankings table puts visibility next to sentiment for each brand. So you can compare yourself with the competitor you come across most often.
  • In the AI model comparison on the Dashboard, you see a row for each prompt with the answers from ChatGPT, Gemini and Claude side by side, with the sentiment label of each answer and whether your brand is mentioned.
  • In the Answers overview and in the detail view of a single answer, you read the full text, with the mentioned brands highlighted.

That the models can differ is anything but a detail for sales. A prospect using ChatGPT may hear a different story from a colleague who opens Gemini or Claude. The comparison per model shows which story belongs to which assistant.

What a negative or mixed tone means for your conversations

A positive answer is nice to read, but a mixed or negative answer is often more useful. It tells you which objection the buyer already has in mind before you speak to them.

Take a fictional example. You sell ERP software to manufacturing companies. In the AI model comparison, the prompt "which ERP supplier suits a mid-sized manufacturer in Belgium?" has an answer labelled Mixed. You open it and read that the supplier is praised for its industry knowledge, but that implementation reportedly takes a long time. A prospect could easily bring that second point to a first meeting.

What you do with it depends on what you read:

  • The objection is valid. Prepare an honest answer: how long a project takes for a comparable customer, what you do about it and what the customer can speed up themselves.
  • The objection is no longer valid. The answer may be based on outdated information. Note it, so you can counter it with a recent reference, and pass it on to marketing. See also Generative engine optimization: what it is and where to start.
  • The tone differs per model. Ask in the conversation which sources the prospect consulted. Then you know what they have probably read.
  • The tone is neutral. You are mentioned without a verdict. That is an opportunity: there is no opinion yet that you need to push back against.

Sentiment analysis says nothing about who the prospect is or how far along they are in their purchase. What you do get is insight into the picture your market is being presented of you. And with that, you can prepare much better for what comes up at the table.

How to read it in the answer details

A label is a summary. The real information is in the answer itself, and this is how you get it out.

Open a prompt in AI Visibility where your brand scores lower than a competitor. In the detail view of an answer, you see the full text with the mentioned brands highlighted, the model, the date, the sentiment label and the score for each mentioned brand. The screen also shows the Tone of the answer.

Pay attention to three things. First the order: are you mentioned first or last? Then the wording around your name: are these facts, recommendations or reservations? And finally the competitor: what are they praised for that you are not?

Then look at several answers to the same prompt. A single answer says little, because it may read differently tomorrow. If the same tone comes back in several answers, you have a pattern, and that is something you can tailor your story to. How to write a prompt the way a buyer would is covered in How to write prompts the way a buyer looks for a supplier.

What you can do now

  • Write three questions a buyer in your market would ask an AI assistant, and look at what ChatGPT, Gemini and Claude answer.
  • Note the sentiment label of each answer and the objection or recommendation it contains. Discuss the objections at your next sales meeting.
  • For every recurring objection, prepare one answer with concrete evidence, such as a reference or a figure from your own practice.

Want to improve the answers you are seeing now? Then Generative engine optimization: what it is and where to start is a good place to begin.