AI Market Research Tutorials: Sentiment, Competitive Intelligence, and Content Gaps

Q: What is AI market research?

AI market research uses large language models and knowledge graphs to analyze unstructured market data — customer reviews, interview transcripts, competitor content, search queries — at scale. The goal is to surface themes, sentiment, gaps, and competitive positioning that would take a human research team weeks to extract manually.

Q: How is sentiment analysis done with knowledge graphs?

Knowledge-graph sentiment analysis goes beyond positive/negative scoring. It maps the concepts customers talk about (price, durability, support, design), groups them into clusters, and shows which themes drive positive or negative sentiment. You see what customers love, what they hate, and the connections between the two.

Q: What is content gap analysis for market research?

Content gap analysis compares what your market is talking about (customer language, search demand, competitor coverage) with what your own content addresses. The structural gaps reveal positioning opportunities — themes the market cares about that nobody is fully serving yet.

Q: Can I use AI for competitive intelligence?

Yes — feed competitor content, reviews, and press coverage into InfraNodus and get a graph of how each competitor is positioned in the topic space. Cluster overlaps reveal direct competition; cluster gaps reveal whitespace where you can differentiate.

AI market research turns messy, unstructured market data — customer reviews, interview transcripts, competitor content, search queries — into a structured map of themes, sentiment, and competitive positioning. Instead of reading through hundreds of pages, you get a graph: clusters of what people care about, the relationships between concepts, and the gaps where nobody is fully serving the market yet.

This section gathers our tutorials for the four hardest jobs in market research. Run customer sentiment analysis that goes beyond positive/negative to show what drives each emotion. Build competitive intelligence graphs that reveal how each competitor is positioned. Conduct innovation strategy research to find emerging trends before they hit mainstream. Use content gap analysis to find positioning whitespace your competitors haven't claimed.

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Tutorials in this Series

Customer Sentiment Analysis

Map customer reviews as a graph of concepts and emotions. See exactly which themes drive positive and negative sentiment — and where the two collide.

Competitive Intelligence

Visualize each competitor's positioning as a knowledge graph. Find the topical clusters they own, the ones they don't, and the whitespace you can claim.

Innovation Strategy & Trend Research

Track emerging discourse across industry sources and surface latent trends before they hit the mainstream — driven by structural gap analysis.

Content Gap Analysis

Find the topics your market discusses that your content doesn't yet address. Each gap is a positioning opportunity.

Why Market Research Needs Knowledge Graphs

The hardest part of market research isn't collecting data — it's making sense of it. Customer reviews, interview transcripts, social posts, and competitor content are unstructured text, and the patterns that matter live in the relationships between concepts, not in any single document. Traditional approaches force you to choose: aggregate (and lose nuance) or read everything (and lose your week).

A knowledge graph solves this by extracting the concepts and their connections automatically, then letting algorithms surface what humans miss: which themes cluster together, which sentiments attach to which features, where competitors crowd and where they don't.

The Four Tutorials Map to the Four Core Tasks

Sentiment — what do customers feel, and about what specifically?
Competitive intelligence — how is the market positioned, and where is the whitespace?
Innovation — what's emerging that nobody is talking about yet?
Content gap — where do my own content and the market's interests fail to meet?

All four use the same InfraNodus pipeline: import unstructured text → build a graph → run community detection → run gap analysis → query the graph with AI. The differences are in the inputs and the framing, not the mechanics.

From Insight to Action

Market research only matters if it changes a decision. Each tutorial in this section ends with a concrete deliverable: a sentiment driver list, a competitive positioning map, a trend brief, or a content gap report. Pair them with the SEO & LLM Optimization section to convert insights into ranking content.

Frequently Asked Questions

What is AI market research?

AI market research uses LLMs and knowledge graphs to extract themes, sentiment, and positioning from unstructured market data — at a speed and depth manual research can't match.

How is sentiment analysis done with knowledge graphs?

Knowledge-graph sentiment analysis maps emotions to specific concepts so you see what drives each feeling — not just an aggregate score.

What is content gap analysis for market research?

Content gap analysis compares your coverage with what the market discusses, surfacing positioning whitespace.

Can I use AI for competitive intelligence?

Yes — see the competitive intelligence tutorial. Feed competitor content into InfraNodus and get a positioning graph in minutes.

Turn Market Data Into a Map You Can Act On

Stop drowning in customer reviews and competitor PDFs. Build a graph of your market and let structure surface what matters.