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GuidesResearch methodsHow to do AI content analysis: A full guide

How to do AI content analysis: A full guide

Last updated

20 December 2023


Dovetail Editorial Team

Reviewed by

Hugh Good

We’re all seeing the power of AI, and its time-saving potential is providing users with many benefits. 

Censuswide carried out a study on over 3,000 professionals across Canada, Germany, the UK, and the USA. Researchers found that generative AI already saves the average worker 1.75 hours a day

Different applications and work processes are already tapping into AI's efficiencies. And content analysis is where AI may have the most to offer.

AI content analysis could be tremendously significant across various industries, from healthcare to consumer marketing and beyond. Synthesizing huge swaths of different forms of data into actionable insights just became much easier. 

Your organization can incorporate AI content analysis protocols to glean insights from customer data, industry stats, and market information. 

Keep reading to learn more about types of AI analysis, how to get started, and the right way to do it as ethical AI usage comes to the forefront.

What is AI content analysis?

AI content analysis uses AI technologies to enhance the process of analyzing vast amounts of content. AI can speed up, organize, and automate content analysis. 

Organizations gather varying forms of data every day, from numerical to pictorial. Historically, we’ve largely categorized data into two forms: Quantitative and qualitative. 

While previous generations of 'smart' systems could analyze quantitative data, AI-powered processes show promise in finding patterns and insights from all types of data.

This type of analysis offers several advantages over conventional techniques. Primarily, AI content analysis is faster: AI algorithms can 'read' through data and find patterns much faster than humans. Analysis occurs in near-real time. 

We can also train AI tools to pick up on insights that humans may miss. AI can pursue virtually invisible patterns, stress-test hypotheses to verify findings, and measure previously unquantifiable things like tone and intent signals.

How do companies use AI content analysis?

AI applications can:

  • Use natural language processing (NLP) to glean understandings from types of text data

  • Use machine learning and algorithms to parse unstructured content data for patterns

  • Work with complex neural networks and artificial intelligence to: 

    • Ask questions

    • Fine-tune analysis queries

    • Discover and verify patterns

    • Prioritize insights to deliver results to key stakeholders

Across industries, we can use many types of AI content analysis for ongoing research. 

Depending on your organization's needs, you can use AI for many tasks. If you need to sift through data for very specific answers, AI has you covered: 

  • Companies can easily verify time sheets and payroll data throughout a year's records.

  • Insurance companies can use AI to comb through social media posts for patterns indicating insurance fraud. 

Ultimately, data scientists and companies can use AI in content analysis for efficiency and to dig deeper into the data. However, the process is not yet robust enough to work without human intervention.

The different types of content AI can analyze

AI can analyze a wide range of data types, including both structured and unstructured data. 

Some of the different formats AI analytics tools can process include:

  • Diagrams

  • Text, including reports, news articles, product descriptions, narratives, how-to guides, etc

  • Numerical data, including research data, financial information, customer usage data, etc

  • Videos and audio information

  • Graphics and other visual content

AI can analyze these formats from human-generated sources, historical records, real-time information, and even AI-generated sources.

Types of AI-powered content analysis

Four of the most popular types of analysis are text, diagram, video, and cross-modal.

Text analysis

AI tools can 'read' text passages and pull out meaningful insights based on the tool's understanding of the text and the user's intentions. 

This process involves extracting explicit and implicit text information. Explicit could be a summary of the information, while implicit could be the tone or intent of the text. 

Some examples of using text analysis include:

  • Summarizing white papers or large research documents into short, tailored summaries

  • Using AI-generated content detection tools to check for human language and creators

  • Learning the context in which a document was written based on language patterns

  • Create high volumes of text, like product descriptions and definitions

  • Generate engaging social media posts based on audience preferences across platforms

  • Transforming the information from a block of text into a helpful outline or mind map

  • Learning a text source's human writing style to create a language model and generate additional AI-generated text

Diagram analysis

Diagram analysis uses AI to extract information from graphs, charts, organizational diagrams, and other pictorial data types. 

There are several different layers of analysis made possible through AI, including:

Diagram identification

When AI tools scan PDFs and use optical character recognition (OCR) to recreate a digital copy, they can recognize diagrams within the text to flag them for a human operator.

Translation of diagrams

More sophisticated AI tools can convert the image into data. They translate the labels and text fields of the diagram into text through OCR. From there, they capture and relay the known values in the diagram to a diagram creator. 

Example: Imagine a prefilled create a chart' popup in Excel that pulled all the figures from a picture image.

Generating new content based on diagrams

After digitizing and configuring diagrams into data, AI tools can generate various content from the information, including summaries, valuable insights, and new diagrams.

Video analysis

AI tools can analyze human behavior in videos, a collection of videos, and information represented through the collective audio-visual elements of videos. 

They can:

  • Identify objects captured by video

  • Monitor and report on interactions of different objects, such as detecting the speed of cars on the highway or determining who was likely at fault in a traffic accident

  • Detect unusual behaviors that could be cause for alarm, such as abnormal movement on a baby monitor or atypical movement sequences in factory equipment

Video analytics has rapidly progressed across all industries thanks to AI. It can recognize objects, predict how they should behave, and take action when they deviate.

Cross-modal analysis

Cross-modal analysis processes use more than one modality to extract information from content. When one modality cannot produce sufficient insights, cross-modal AI tools supplement the data with other types to draw more complete insights or conclusions. 

Cross-modal analysis techniques include “binary-value representation” and “real-value representation.”

AI applications can identify and compare data from different modalities through binary-value analysis. In real-value representation, AI combines multiple types of information to draw a conclusion.

Example: While AI can generate some conclusions from traffic footage regarding drivers' behaviors at a critical intersection, that may not be enough.

It might also use traffic data reports from previous years or metadata from Wi-Fi-enabled dashcams to supplement its insights. This can help it find more robust patterns or make more accurate conclusions.

What should you include in a content analysis?

AI content analysis should include:

  • A specific query or objective to guide the analysis

  • A specific sample set of data or content to analyze

  • Detailed workflows for generating and sharing the results of the analysis

  • Disclaimers and best practices for using the results of the analysis so audiences understand the limitations of the insights and how to use them best

Seven steps for efficient AI content analysis

Wondering how to do AI content analysis? It’s a complex, multi-step process. For data you can trust, thorough preparation is your best friend. 

Follow these seven steps for consistent results:

1. Define the objective of the analysis

Create a clear question or research objective for the project. This will help you select the right data sources and content to add to the analysis. It’ll also guide the AI in identifying the most valuable insights and conclusions. 

Some example objectives of an analysis might be:

  • Determining the tone of content examples through semantic analysis tools

  • Identifying specific business inefficiencies from hidden patterns in a sample of content

  • Uncovering weaknesses in marketing content for revision or rewriting

The objective can be simple or complex as long as the content and AI tool support it.

2. Collect and prepare AI-generated content

Next, identify the collection of content to analyze. For example, you might select: 

  • Communications from a single author

  • Reports generated over a certain annual period,

  • Customer interactions through a chatbot from a sub-domain of your company website

Restricting the content set can be just as important as creating a specific objective.

3. Choose appropriate analytical tools and methods

Dozens of analytical tools are available, incorporating AI and machine learning to different degrees and through proprietary algorithms. 

Your organization may already have licenses for specific tools, or you may need to purchase new tools with specific capabilities.

Ensure the tool supports the analysis method you need, whether you’re opting for simple text-based analysis or a complex cross-modal approach.

4. Analyze the content for patterns and insights

The AI application should do most of the heavy lifting here. The program will have a library of algorithms to translate the content into usable data, identify patterns, and generate conclusions or insights. 

Some AI tools answer queries or follow instructions—similar to ChatGPT—while others have a menu of functions to work with.

5. Interpret the results in the context of the objective

Once the program analyzes the content and creates an accurate analysis, it generates its findings for review. 

Outputs could include:

  • Summaries of the content, including insights, conclusions, and recommended actions

  • Answers to direct queries

  • Quantitative reports

  • Personalized recommendations

  • Notes and highlights added to the original media

You and other stakeholders can review these outputs for accuracy and act on the insights.

6. Document the findings and conclusions

The outputs can act as documentation for your findings and subsequent actions. 

However, it’s vital to have a human intermediary. They should: 

  • Review the AI’s findings

  • Determine what actions to take as a result of the analysis

  • Intervene if the AI appears to be malfunctioning or making incorrect assumptions

Based on your judgment of the analysis and its conclusions, you can move forward with your business objective.

7. Present the analysis to relevant stakeholders

Communicate the outcomes of the analysis to stakeholders, such as:

  • Leadership who want the analysis as a foundation for strategy decisions, budgetary changes, or new programs

  • Employees, especially if the results indicate a need for new workflows or work processes

  • Teams and departments who can use the insights to be more productive and make more informed, data-driven decisions

Ethical considerations in AI content analysis

AI content analysis is a powerful tool, and AI content analysis is seeing exciting developments. 

Organizations of all sizes and industries can use AI tools to analyze information. Whether they want to examine product usage, employee output, or customer feedback, AI content analysis can help them make informed decisions.

However, AI usage continues to pose several ethical challenges for all users, including:

Permission to use the data

Companies must obtain consent to use consumers' information, whether they collect it directly or acquire it from third-party sources. 

Many consumers worry about companies misusing their data, impacting brand reputation and customer trust. Countries and industries are heavily regulating data usage, so it’s important to stay on top of emerging laws.

Implicit biases within AI tools

As AI tools learn from human content, any bias in the original material can result in unethical, biased conclusions. 

Organizations must ensure people thoroughly vet results and recommendations before implementing them.


What is the first step in content analysis?

The first step in high-quality content analysis is to determine your objective. 

Unfocused AI content analysis can lead to a scattergun approach and error-filled conclusions. 

In-depth analyses can also use a lot of resources and time, so structuring your objective to be as specific as possible plays a critical role in the project’s effectiveness.

Can AI generate insights?

Yes, AI can generate insights, conclusions, and recommended actions from the analysis. However, generative AI is constrained by the content samples it can access and how well-trained the programs are.

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