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The Real Impact of Using MonkeyLearn for Automated Data Analysis
AI is everywhere now.
Especially in business.
And nowhere is it making bigger waves than in how we crunch numbers.
I’m talking Data Analysis and Business Intelligence.
It used to be a slow, manual grind.
Pulling data, cleaning it, trying to make sense of it.
Hours lost staring at spreadsheets.
Or worse, text documents.
Trying to find patterns in mountains of words.
Customer feedback, social media comments, support tickets.
Forget it.
Too much noise.
Too little time.
But things changed.
AI showed up.
And tools like MonkeyLearn popped up.
Tools designed to handle the messy stuff.
To turn text data into something useful.
Something you can actually use to make decisions.
Fast.
If you’re in Data Analysis and Business Intelligence, you need to pay attention.
Because MonkeyLearn isn’t just another tool.
It’s a different way of working.
A way that saves you time.
A way that gets you answers.
Without the headaches.
This is about getting real impact from your data.
Without getting bogged down in the process.
Let’s break it down.
Table of Contents
- What is MonkeyLearn?
- Key Features of MonkeyLearn for Automated Data Analysis
- Benefits of Using MonkeyLearn for Data Analysis and Business Intelligence
- Pricing & Plans
- Hands-On Experience / Use Cases
- Who Should Use MonkeyLearn?
- How to Make Money Using MonkeyLearn
- Limitations and Considerations
- Final Thoughts
- Frequently Asked Questions
What is MonkeyLearn?
So, what exactly is this thing, MonkeyLearn?
Think of it as your AI sidekick for text data.
Specifically, for getting useful stuff out of text that would usually take forever to read through.
Customer reviews, emails, social media posts, open-ended survey responses.
All that unstructured text.
MonkeyLearn uses machine learning to understand it.
It can classify text.
Like sorting feedback into ‘positive’, ‘negative’, or ‘suggestion’.
It can extract data.
Pulling out company names, product mentions, or specific issues.
It can even figure out sentiment.
Was the customer happy, angry, or neutral?
The big win here is automation.
You don’t have to read every single comment.
MonkeyLearn does the heavy lifting.
It processes massive volumes of text data automatically.
This is huge for anyone in Data Analysis and Business Intelligence.
You get insights at scale.
Fast.
Instead of spending days manually tagging data, you can set up MonkeyLearn.
Train it on a small sample of your data.
Then let it rip on the rest.
It learns from your examples.
Gets better over time.
The goal?
To turn confusing text into structured, actionable data.
Data you can easily analyse.
Plot on charts.
Share with your team.
Make decisions based on.
It’s built for people who need to understand their customers, their market, or their internal processes through text data.
Without getting buried alive in it.
Marketers trying to gauge campaign sentiment.
Product teams understanding user feedback.
Support departments categorising tickets.
Researchers analysing open-ended survey responses.
Anyone drowning in text data.
That’s who MonkeyLearn is for.
It takes the grunt work out of text analysis.
Leaving you free to do the stuff that actually matters.
Like finding the insights.
And acting on them.
That’s the core idea.
Automated understanding of text data.
Simple.
But powerful.
Key Features of MonkeyLearn for Automated Data Analysis
Okay, so what does MonkeyLearn actually do?
What are the buttons and levers that make it tick for Automated Data Analysis?
It’s got a few key features that are serious game-changers.
- Text Classification:
This is probably the most used feature.
You feed it text, it assigns tags or categories.
Imagine having thousands of customer reviews.
You need to know which ones are about ‘pricing’, which are about ‘customer service’, which are about ‘product quality’.
Manually? Forget it.
MonkeyLearn lets you create your own categories.
You train it by showing it examples of text that fit each category.
Maybe you tag 50 reviews for each category.
Then you hit go.
It reads the rest of your reviews and tags them automatically.
This turns unstructured text into structured data.
Suddenly, you can see that 40% of your reviews are about pricing issues.
And 30% praise customer service.
That’s actionable.
- Sentiment Analysis:
This takes classification a step further.
It’s not just about *what* they are talking about.
It’s *how* they feel about it.
MonkeyLearn can detect if text is positive, negative, or neutral.
You can apply this to tweets about your brand.
See if overall sentiment is going up or down.
Apply it to product reviews.
Find out which features customers love and which they hate.
Again, you can train it.
Maybe you have industry-specific language or slang.
You teach MonkeyLearn how to interpret sentiment in your specific context.
This is powerful for measuring brand perception, customer satisfaction, and the impact of changes you make.
- Topic Extraction:
Sometimes you don’t need predefined categories.
You just need to know what the main topics are within a body of text.
MonkeyLearn can identify key phrases or topics automatically.
Feed it a batch of support tickets.
It might tell you the top topics are ‘login issues’, ‘billing questions’, ‘feature request’.
This is unsupervised learning.
You don’t train it beforehand.
It finds the patterns itself.
Great for exploring new data sets.
Or understanding what’s trending in customer conversations without having prior assumptions.
- Keyword Extraction:
Similar to topic extraction, but focused on specific words or phrases.
Want to find the most frequently mentioned products in reviews?
Or the key features customers are talking about?
Keyword extraction pulls these out.
You can see which terms appear most often and in what context.
This is crucial for understanding product usage, identifying common problems, or finding trending topics related to your business.
- Integrations:
Data doesn’t live in a vacuum.
MonkeyLearn needs to connect to where your data is.
It has integrations with popular tools.
Things like Excel, Google Sheets, Zapier, SurveyMonkey, Zendesk, and many more.
This means you can automate the entire workflow.
Data comes in from a survey platform.
Goes straight into MonkeyLearn for analysis.
The results get sent to a Google Sheet or a dashboard.
No manual export/import steps.
This seamless flow is essential for real Automated Data Analysis.
It makes it easy to process data as it arrives.
Providing near real-time insights.
- Custom Models:
This is where the power really kicks in.
Generic models are okay.
But your data is unique.
MonkeyLearn lets you build and train your own models.
You define the categories or entities you want to find.
You train the model using your specific data.
This means the models are highly accurate for *your* use case.
Analysing legal documents requires different understanding than analysing restaurant reviews.
Custom models let you tailor the AI to your exact needs.
This isn’t just off-the-shelf AI.
It’s AI that learns from *your* business.
Leading to much better results and more precise insights.
These features combined make MonkeyLearn a powerful engine for turning messy text into clean, structured, actionable data.
It automates the hard parts.
Frees you up.
Lets you focus on the ‘analysis’ part of Data Analysis and Business Intelligence.
Benefits of Using MonkeyLearn for Data Analysis and Business Intelligence

Okay, you know the features.
But what does that actually *mean* for you?
What are the real-world benefits of using MonkeyLearn, especially for Data Analysis and Business Intelligence professionals focused on automation?
It boils down to a few key things.
- Massive Time Savings:
This is probably the biggest win.
Manually processing text data is brutally slow.
Reading hundreds or thousands of comments.
Trying to tag them consistently.
It takes forever.
MonkeyLearn can process data at scale in minutes or hours.
What used to take a team days now takes one tool a fraction of that time.
This frees up your time.
Your analysts’ time.
Time you can spend actually *analysing* the data.
Finding insights.
Building reports.
Making recommendations.
Instead of doing the mind-numbing data entry or tagging.
- Improved Data Quality and Consistency:
Humans are inconsistent.
Especially when doing repetitive tasks like data tagging.
What one person tags as ‘Feature Request’, another might tag as ‘Suggestion’.
Fatigue sets in.
Accuracy drops.
An AI model, once trained, applies the same rules every single time.
This means your data is tagged consistently.
Leading to higher quality data.
And more reliable analysis.
You can trust the classifications and extractions.
This is crucial for making confident business decisions.
- Faster Insights:
If it takes you weeks to process customer feedback, the insights you get are old.
By the time you act, the situation might have changed.
With Automated Data Analysis using MonkeyLearn, you can process data much faster.
Get insights in near real-time.
Spot trends as they emerge.
Identify problems early.
This speed gives you a competitive edge.
Allows you to be agile.
Respond quickly to customer needs or market shifts.
- Scalability:
What happens when your data volume doubles?
Or triples?
Manually processing it means hiring more people.
Training them.
It’s expensive and slow to scale.
MonkeyLearn scales easily.
Whether you have a hundred pieces of text or a million, the process is largely the same.
You can handle massive data volumes without linearly increasing costs or staffing.
This is essential for growing businesses.
- Deep Understanding of Unstructured Data:
Traditional data analysis tools are built for structured data.
Numbers in columns and rows.
But a huge amount of valuable business data is unstructured text.
MonkeyLearn unlocks this data.
Turns the noise into signals.
Lets you understand the nuances of customer feedback, market sentiment, or internal communications in a way that wasn’t possible before.
You can dig deeper into the ‘why’ behind the numbers.
- Cost Reduction:
Think about the cost of manual data processing.
Staff hours spent on repetitive tasks.
Hiring temporary staff for data clean-up.
MonkeyLearn automates these tasks.
Reducing the need for manual effort.
This directly impacts operational costs.
Allows you to allocate human resources to higher-value activities.
Getting these benefits means transforming your workflow.
Moving from slow, manual, inconsistent processes to fast, automated, reliable ones.
For anyone serious about getting value from text data in Data Analysis and Business Intelligence, these benefits are hard to ignore.
It’s about working smarter.
Getting more done.
With better results.
Pricing & Plans
Alright, let’s talk numbers.
How much does this AI magic cost?
MonkeyLearn isn’t a free tool forever.
They have a free trial or a free tier to get you started.
This is cool.
Lets you test it out with your own data.
See if it actually works for your specific needs.
Before you commit any cash.
The free plan usually has limits.
Limited number of transactions (pieces of text analysed).
Limited access to features.
But it’s enough to build a small custom model.
Or run a small batch of data through a pre-trained model.
It’s a good way to kick the tires.
For serious use, you’ll need a paid plan.
Their pricing is typically based on usage.
Specifically, the number of queries or transactions you run through the API or platform.
Think of it like credits.
Each time MonkeyLearn processes a piece of text, it uses a credit.
Plans start at a certain number of transactions per month.
And go up from there.
The more data you need to process, the higher the plan you’ll need.
They also offer different levels of features.
Higher tiers might include more advanced model training options.
Better support.
Access to enterprise-level integrations.
This usage-based pricing is pretty standard for AI text analysis tools.
It scales with your needs.
If you have a quiet month, you use fewer credits.
If you launch a huge survey or marketing campaign, you scale up.
How does it compare to alternatives?
There are other players in the text analysis space.
Some are more general-purpose AI platforms (like Google Cloud Natural Language or AWS Comprehend).
These are powerful but can be more technical.
Require more developer input.
MonkeyLearn positions itself as more user-friendly.
Designed for business users, not just data scientists.
Its pricing is often competitive for its ease of use and custom model capabilities.
Other tools might be more focused on specific tasks, like just sentiment analysis.
MonkeyLearn offers a suite of text analysis tools in one place.
The value comes from the time saved and the quality of insights.
If MonkeyLearn saves your team dozens or hundreds of hours a month, the cost of the subscription is likely a fraction of the salary cost you’re avoiding.
Plus, you get insights you simply couldn’t get manually.
Or not fast enough to be useful.
So, while it’s not free, the potential ROI for businesses dealing with text data in Data Analysis and Business Intelligence is significant.
Start with the free trial.
See how many transactions you use for a sample of your data.
Project that out to your full data volume.
Then look at their pricing tiers.
Compare it to the cost and time of doing it manually.
Or using other tools.
That’s how you figure out if it makes sense for your budget.
Hands-On Experience / Use Cases

Okay, theory is nice.
But what’s it like to actually use MonkeyLearn?
Let’s run through a typical scenario.
Suppose you’ve just run a big customer survey.
Thousands of responses.
Many have open-ended comments asking for feedback.
You need to understand the main themes and sentiment.
Fast.
Trying to read all of them is impossible.
First, you’d log into MonkeyLearn.
You’d choose to build a custom model.
Let’s say a text classifier.
You define the categories you care about: ‘Product Feature Request’, ‘Bug Report’, ‘Customer Service Feedback’, ‘Ease of Use’, ‘Pricing Comment’, ‘General Praise’.
You upload a sample of your survey data.
Maybe 100-200 comments.
MonkeyLearn shows you these comments one by one (or in batches).
You read each comment and assign the correct category.
If a comment is about a new feature idea, you tag it ‘Product Feature Request’.
If it mentions a problem with logging in, you tag it ‘Bug Report’.
This is the training phase.
You’re teaching the AI your specific criteria.
MonkeyLearn learns from your tags.
Once you’ve tagged enough examples (MonkeyLearn often tells you when it has enough data to start predicting), you train the model.
This takes a few minutes.
Then you can test the model.
Feed it some new comments and see if it predicts the categories correctly.
If not, you train it on more examples.
Refine your categories if needed.
The interface is pretty intuitive for this.
No coding required.
Just clicking and tagging.
Once your model is accurate enough (say, 80-90%+), you’re ready for the main event.
You upload the rest of your survey comments.
Thousands of them.
You run them through your newly trained classifier.
In minutes, MonkeyLearn processes the entire dataset.
Every comment is assigned one or more categories.
You can then export the results.
A spreadsheet with the original comment and its assigned category (or categories).
Now you have structured data.
You can easily pivot table it.
Count how many comments are in each category.
Filter for all ‘Bug Report’ comments.
Combine this with sentiment analysis.
Run the same data through a sentiment model.
Now you know not just *what* they talked about, but *how they felt* about it.
You can see, for example, that ‘Pricing Comments’ have 80% negative sentiment.
While ‘Ease of Use’ comments are 95% positive.
This is powerful for Automated Data Analysis.
You went from a messy pile of text to clear, quantifiable insights.
Without reading every single response.
Other use cases?
Analysing support tickets to identify trending issues.
Categorising emails to route them correctly.
Extracting key data points (like company names, dates, amounts) from reports or documents.
Analysing social media mentions to understand brand perception during a campaign.
The usability is high for non-technical users.
Building and training models is guided.
Connecting data sources is straightforward with integrations.
The results are exportable in standard formats, ready for your favourite BI tool (like Tableau, Power BI, or even Excel/Google Sheets).
It genuinely simplifies complex text analysis tasks.
Turning something that felt overwhelming into a manageable, automated process.
Who Should Use MonkeyLearn?
Is MonkeyLearn for everyone?
Probably not.
It’s pretty specific to businesses that generate a significant amount of unstructured text data and need to analyse it systematically.
So, who are the ideal users?
- Data Analysts and Business Intelligence Professionals:
Obviously.
If your job involves getting insights from data, and some of that data is text, MonkeyLearn is built for you.
It automates the manual grunt work.
Lets you focus on the higher-level analysis and reporting.
It’s a tool to make your existing work faster and better.
- Marketing Teams:
Marketers deal with text all the time.
Social media comments, campaign feedback, ad reviews, market research surveys.
MonkeyLearn helps them understand brand sentiment, track campaign performance based on customer reactions, and get actionable insights from qualitative feedback at scale.
Great for measuring the real impact of marketing efforts beyond simple engagement metrics.
- Product Teams:
User feedback is gold for product development.
Reviews, feature requests, bug reports, usability testing notes.
MonkeyLearn can automatically categorise and analyse this feedback.
Identify the most requested features, the most common bugs, or areas causing frustration.
Helps product managers prioritise their roadmap based on real user needs quantified from text data.
- Customer Support/Success Teams:
Support tickets, customer emails, chat logs, feedback forms.
This is a mountain of text.
MonkeyLearn can automatically classify support tickets by issue type.
Route them to the right team.
Identify trending issues before they become major problems.
Measure customer sentiment in interactions.
Leads to faster resolution times and improved customer satisfaction.
- Researchers:
Whether academic or market researchers, text data is common.
Analysing open-ended survey questions, interviews, articles, or social media discussions.
MonkeyLearn helps process large qualitative datasets.
Identify themes, sentiments, and keywords across responses.
Makes qualitative analysis more systematic and scalable.
- Small Businesses and Agencies:
You might not have a dedicated data science team.
But you still get customer feedback or online mentions.
MonkeyLearn provides a user-friendly way to get value from that text data without needing deep technical expertise.
Agencies can use it to offer text analysis services to their clients.
So, if you’re struggling to make sense of text data because there’s simply too much of it, or it takes too long to process manually, MonkeyLearn is likely a good fit.
It’s designed for teams and individuals who need actionable insights from text.
Without getting bogged down in the mechanics of text processing.
It’s less for someone who just needs to write an email.
And more for someone who needs to analyse hundreds of emails.
That’s the distinction.
It’s a data analysis tool first.
Using AI to handle the messy text part.
How to Make Money Using MonkeyLearn

Alright, let’s talk coin.
Can you actually make money with MonkeyLearn?
Short answer: Yes.
Not directly by using the tool itself like a content generator you sell output from.
But by leveraging its capabilities to provide valuable services or dramatically improve efficiency.
Think of it as an force multiplier for data analysis tasks involving text.
- Offer Text Analysis Services:
This is the most direct way.
Many businesses have text data but lack the tools or expertise to analyse it.
Customer reviews, survey responses, social media mentions, internal communications.
You can offer a service to process this data for them.
Using MonkeyLearn, you can quickly perform sentiment analysis, topic extraction, and text classification.
Deliver structured data and clear reports.
Charge a fee for this service.
Your efficiency with MonkeyLearn means you can take on more clients or projects than if you were doing it manually.
Example: A small marketing agency doesn’t have the capacity to analyse thousands of online reviews for their clients. You offer a monthly service using MonkeyLearn to provide them with a summary of review sentiment, key topics, and trending issues across all platforms.
- Improve Efficiency in Data Analysis Projects:
If you’re already doing Data Analysis and Business Intelligence work, using MonkeyLearn for the text component makes you faster.
Much faster.
If a project involves analysing customer feedback from surveys or support tickets, MonkeyLearn shrinks the time needed for that part from days to hours.
This means you can complete projects faster.
Take on more projects.
Or deliver results quicker, which clients often value and are willing to pay a premium for.
Efficiency directly translates to increased earning potential, whether you’re a freelancer charging per project or an employee proving your value.
- Product Development Insights:
For product managers or teams, MonkeyLearn helps identify high-impact features requested by users or critical bugs.
Faster, better insights lead to better product decisions.
Building products that customers actually want and need.
This results in more successful products.
Increased user adoption.
Higher revenue.
The insights generated by MonkeyLearn contribute directly to the bottom line through improved product-market fit.
- Optimise Customer Support:
Automatically classifying support tickets means faster routing.
Identifying trending issues quickly allows for proactive solutions (like updating an FAQ or releasing a patch).
Measuring sentiment in support interactions helps identify areas for agent training.
These efficiencies and improvements lead to lower support costs and higher customer retention.
Retained customers spend more over time.
Reducing churn is a direct path to increasing revenue.
Real case study example (simulated):
John was a freelance data analyst. He spent 50% of his time manually sorting and tagging customer feedback for clients. He could only handle two or three projects like this per month. He started using MonkeyLearn. He built custom classifiers in hours instead of days. He could now take on five or six projects per month. His revenue doubled within three months, while his working hours stayed the same. He started charging a premium for “AI-powered rapid text analysis.” He wasn’t just selling analysis; he was selling speed and scale powered by MonkeyLearn.
MonkeyLearn makes you more capable and more efficient at handling text data.
This capability and efficiency are highly valuable to businesses.
Whether you package it as a specific service, use it to enhance your existing offerings, or leverage it internally to drive better business outcomes, there’s a clear path to increasing your earning potential by integrating MonkeyLearn into your workflow.
It’s a tool that helps you deliver more value.
And value is what clients pay for.
Limitations and Considerations
Okay, it’s not all sunshine and automated rainbows.
MonkeyLearn is powerful, but it has limits.
And there are things you need to keep in mind.
It’s AI, not magic.
- Accuracy Isn’t 100% (Initially):
AI models are only as good as the data they’re trained on.
When you build a custom model, you need to provide high-quality, correctly labelled examples.
The initial accuracy might not be perfect.
You need to test it.
Provide more training data.
Refine your categories or extraction rules.
Achieving high accuracy takes effort and iteration.
It’s not a “push button and get perfect results” situation from day one.
- Garbage In, Garbage Out:
If your input data is messy, inconsistent, or irrelevant, MonkeyLearn will struggle.
You still need relatively clean text data to feed into it.
Spelling errors, poor grammar, or highly ambiguous language can reduce accuracy.
Preprocessing your data might still be necessary depending on the source.
- Learning Curve:
While designed for business users, there’s still a learning curve.
You need to understand concepts like training data, testing models, and interpreting metrics like precision and recall.
Building effective custom models takes practice and understanding of your data and goals.
It’s not hard, but it requires some investment in learning the platform and the basics of how these models work.
- Doesn’t Understand Nuance/Context Perfectly:
AI is getting better, but it’s not human.
It can sometimes miss subtle sarcasm, complex irony, or context-dependent meaning that a human would easily grasp.
This is less of an issue for general sentiment or topic analysis on large volumes, but for deep qualitative analysis of individual pieces of text, human review is still often necessary.
The Automated Data Analysis is powerful for scale, but don’t ditch human insight entirely.
- Cost Can Add Up:
As mentioned, pricing is usage-based.
If you have massive volumes of data, the costs can become significant.
It’s important to monitor your usage and choose a plan that fits your needs and budget.
Make sure the ROI (time saved, insights gained) justifies the expenditure.
- Integration Limitations:
While MonkeyLearn has many integrations, it might not connect directly to every single tool you use.
You might need to use intermediaries like Zapier or export/import data manually if a direct connection isn’t available.
These aren’t dealbreakers, but they are factors to consider.
MonkeyLearn excels at turning unstructured text into structured data for analysis.
It’s a pre-processing engine.
It doesn’t replace your favourite BI dashboard or statistical analysis software.
You still need tools to visualise and further analyse the structured data it provides.
Knowing these limitations means you can set realistic expectations.
Plan your workflow effectively.
Use MonkeyLearn where it’s strongest.
Which is high-volume, repetitive text analysis tasks that feed into your broader Data Analysis and Business Intelligence processes.
It’s a tool to enhance, not entirely replace, human expertise.
Final Thoughts
So, where does that leave us with MonkeyLearn?
If you’re drowning in text data.
Struggling to get insights from customer feedback, reviews, social media, or open-ended survey responses.
Or spending way too much time manually tagging and categorising text.
MonkeyLearn is absolutely worth looking into.
Especially if you’re in the Data Analysis and Business Intelligence space.
Its core strength is Automated Data Analysis for text.
It turns a time-consuming, manual, and often inconsistent process into something fast, scalable, and reliable.
The ability to build custom models tailored to your specific business language and needs is a major differentiator.
It means you get highly relevant and accurate results.
The benefits are clear: significant time savings, improved data quality, faster access to insights, and the ability to process data at scale.
These aren’t small wins.
They directly impact your efficiency, the quality of your analysis, and your ability to make informed decisions quickly.
While there’s a learning curve and it’s not a magic bullet for perfectly understanding every piece of text, for the vast majority of use cases in business text analysis, it delivers.
It frees you up from the tedious task of data preparation.
Lets you focus on the actual analysis and strategy.
The cost is a factor, but weigh it against the cost of manual labour and the value of timely insights.
For many businesses, the ROI will be compelling.
My recommendation?
If text data is a bottleneck in your workflow, give MonkeyLearn a serious look.
Start with the free trial.
Upload a sample of your data.
Build a simple classifier or extractor.
See how quickly you can get structured data from your unstructured text.
Experience the automation first-hand.
It might just change how you approach text analysis forever.
It certainly changed mine.
It’s a tool that makes you more effective.
More productive.
And ultimately, helps you get more value from your data.
Visit the official MonkeyLearn website
Frequently Asked Questions
1. What is MonkeyLearn used for?
MonkeyLearn is primarily used for text analysis and automation.
It helps businesses analyse unstructured text data.
Tasks include text classification, sentiment analysis, and keyword extraction.
It turns text into structured data for easier analysis and reporting.
2. Is MonkeyLearn free?
MonkeyLearn offers a free trial or a limited free plan.
This allows you to test its capabilities.
For larger scale use or more advanced features, paid plans are required.
Pricing is typically based on the volume of text processed.
3. How does MonkeyLearn compare to other AI tools?
MonkeyLearn focuses specifically on text analysis and is designed for business users.
It’s generally considered more user-friendly for custom text classification than some broad AI platforms.
It offers a suite of text analysis features in one platform.
Other tools might specialise in different AI areas or require more technical expertise.
4. Can beginners use MonkeyLearn?
Yes, MonkeyLearn is designed with a user-friendly interface.
You don’t need to be a data scientist to use it.
Building custom models is guided.
However, understanding basic data analysis concepts is helpful for getting the most out of it.
5. Does the content created by MonkeyLearn meet quality and optimization standards?
MonkeyLearn doesn’t “create” content in the writing sense.
It processes and analyses existing text content.
It extracts information and provides structured data.
The quality of its analysis depends on the training data and the complexity of the text.
It helps *you* gain insights to improve your own content and optimisation based on data.
6. Can I make money with MonkeyLearn?
Yes, you can make money by using MonkeyLearn.
You can offer text analysis services to clients.
It increases your efficiency in data analysis projects, allowing you to take on more work.
It helps businesses make better product or customer service decisions that lead to revenue growth.
It’s a tool for value creation through efficient text data analysis.