Posted Wednesday, July 9, 2025
AI text analysis can be used to identify patterns in customer feedback, survey responses, scientific papers, social media posts, and other types of text data. It can help businesses to understand customer sentiment, identify trends, and make data-driven decisions that address customers' needs, gaps in research, and untapped market opportunities.
The most common use case for AI text analysis is to upload data to ChatGPT and ask it about sentiment and insights. However, this approach will produce generic results that will converge to the ground truth that all models are trained to provide, which means you'll get the same insights as your colleagues or competitors.
That's where InfraNodus can be very helpful. It represents a text as a knowledge graph, which allows it to apply advanced NLP and network analysis algorithms to detect the most relevant concepts and identify the main topical clusters and reveal the gaps between them. Additionally, it can use built-in sentiment analysis to show the topical insights by segment: for instance, the main topics in negative vs. positive customer reviews or the main topics in a certain segment (e.g. respondents from a certain location or age group).
Here are some of the most common use cases for text analysis:
One of the most powerful features of InfraNodus AI text analyzer is its ability to detect the main topics in a document or a text corpus. This information can be used to get a general overview of content (like a visual summary) with the added benefit of having a clear structured view of the main ideas and their relationships.
Users can zoom in on any topical clusters to see the parts of the text that relate to it as well as getting a detailed view of the main concepts inside. The built-in AI can then generate a summary for this topic or research questions and insights that can help develop this topic further — both in the context of the document's knowledge graph or transcending the context to link it to other disciplines and ideas.
Every statement is tagged with a sentiment score using the built-in Google Cloud AI. This score can be used to filter the knowledge graph and compare the main topics for the positive or negative sentiment. This can be particularly useful for understanding the strong sides of a product and the weakness of competitors. These insights can be used both for product development and marketing campaigns.
Based on the topical structure of a graph, we can identify the topics that are least connected to each other. Structural holes between these topics are the content gaps. LLMs can be used to generate research questions and ideas that bridge these gaps to generate relevant insights that are novel because they connect ideas that relate to the context in a new way.
Entity detection can be used to build a knowledge graph from text that represents the main entities as the nodes and their relations as the edges. This knowledge graph will provide structured insights about the main elements of the text and help uncover hidden patterns. Certain concepts or named entities detected in a document can be selected to see where they appear in original context. Built-in AI can be used to generate new ideas based on them.
Additionally, knowledge graphs can be used to provide higher-quality responses for AI-based LLM workflows replacing the traditional RAG with GraphRAG that offers deeper insights about the relations in a document.
One of the most typical use cases for AI text analyzers is to generate a summary for a document or a text corpus. Knowledge graph representation can provide better insights and augment AI-generated summary with topical insights.
AI text analyzers can be used to classify documents or text corpora based on their content. This can be applied to spreadsheet data (e.g. customer surveys or market research data) as well as a corpus of documents that needs to be organized into categories. Classification can also be used to develop ontologies and taxonomies. Bipartite networks can be built that combine documents and concepts to reveal how the documents are embedded into topical clusters.
There are several types of AI text analyzer tools. Some of them have a basic chat interface and can be used for rudimentary tasks such as summarization and insight extraction. However, more advanced workshops may require specialized interfaces that can be used to analyze topical structure by segments and derive additional insights based on a certain criteria.
To learn more, please, see our article on AI text analysis tools.
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