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No-Code Artificial Intelligence (AI)

AI Natural Language Processing (NLP) and Text Categorization

Natural Language Processing (NLP) focuses on enabling computers to understand and interact with human language through algorithms and models.

Text categorization, a key NLP task, involves automatically assigning labels to text based on its content, with applications in spam detection, sentiment analysis, and more.

By training machine learning models on labeled data, NLP systems can efficiently classify new texts, aiding in the organization of large volumes of information.

AI NLP Block

Simply drag-and-drop the following block within your no-code app editor:

Connect your block with other blocks inside your editor:

Click the edit button to specify your action:

You will be presented with an editor that contains the following options:

  • Select a trained AI model
  • Upload MS Excel
  • Enter classification manually
  • All classifications
  • Keywords
  • Testing AI

The actual text editor can be used to insert text. You can also insert previous blocks (if any) via drag-and-drop, and during runtime the system will automatically insert data from previous blocks into as values.

To train an AI data model, you must feed it data. That is, you are teaching the algorithm to recognize patterns in your text and to classify each pattern. This method allows you to provide several patterns, and the system should be able to identify the correct categorization for the given text during runtime. Please keep in mind that the more data you supply to AI, the better it will perform.

Language

You must always select your language, as the system supports multiple languages, each requiring its own training.

Model Name and Description

Please provide the name and description of your AI model.

Select a trained AI model

This option allows you to pick a previously trained AI model. This allows you to reuse the same AI data model across several business apps.

Please keep in mind that any modifications to the AI model will be reflected in all other business apps.

Upload MS Excel

Using this option you can upload your MS Excel file. Your MS Excel file should only contain two columns. The first column must include the classification name. The second column contains the data.

Enter classification manually

You can manually enter your data using this option. The first field is for categorization, while the second is for real data.

All classifications

This part is identical to the preceding one, with the exception that you will only see it if you already have classifications (entered manually or by MS Excel file upload).

Keywords

If you need to fine-tune your data models, this is the part to do it in. For each of your classifications, you may define which keywords must be present or must be absent. This assists the algorithm in determining how to find the correct categorization with a higher likelihood.

Testing AI

Manual Test (AI)

To run a manual test, click the button shown below.

In this part, you may quickly see how successfully your data model has been trained. Enter any keyword and click 'TEST DATA MODEL' to get the results. If the algorithm isn't doing well, go back to training your model and either add additional data or change your keywords to help the system identify the correct categorization.

Automated Test (AI)

To run an automated test, click the corresponding button as shown.

If your model is functioning correctly, you’ll see a confirmation screen like the one below.

If there are issues, an error screen will appear.

If you encounter any errors, review your model for potential corrections. One common issue is using overly similar or generic text to identify different categories. To resolve this, revise your category descriptions to make them more distinct and topic-focused. This helps the system accurately differentiate between categories and improves test results.

Running Apps with AI NLP

When you launch your business app, the system will recognize your text classification automatically. It may be used to pick an application execution route, trigger some events (such as sending emails or notifications), create documents, transmit data to REST APIs, and hundreds of more possibilities, depending on your situation. In the following example, the program was merely intended to identify text categorization and deliver it via UI:

Text Classification Example

AI text classification involves teaching computers to categorize chunks of text into different groups based on their topics. It's akin to training an intelligent robotic brain to comprehend written content. Through sophisticated mathematical algorithms and linguistic techniques, these AI systems analyze the words and patterns within the text to determine its appropriate group. Achieving proficiency requires extensive practice with labelled examples, where the text is already associated with its correct category. Once trained, these systems excel at sorting new text snippets into the correct groups. This technology proves immensely useful for various applications, such as identifying spam emails, discerning sentiment from written expressions, and facilitating content discovery tailored to individual interests, be it articles or videos.
⭐ In this example, we'll delve into a concept akin to the previous one, yet focusing on the potential of AI text classification. Within our Fenorri editor, we'll craft a basic diagram featuring Start, AI NLP (Natural Language Processing), Form, and End blocks.
🔧 In this setup, the AI NLP block will manage text classification tasks, while the Form block will serve as a data input form, specifically featuring a text field designed to receive the output from the AI NLP block. This straightforward example aims to demonstrate the application of text classification within a no-code solution. To initiate editing, let's select the AI NLP block and then click on 'Edit'.
✨ Upon accessing the interface, we can create our text classification training data model. Although we have the option to upload an Excel file, for this example, we'll input all our text classifications manually. Specifically, we'll provide 5 text samples exemplifying a happy mood and another 5 text samples depicting a sad mood:
HAPPYI'm over the moon!
HAPPYToday is a great day!
HAPPYI couldn't be happier!
HAPPYLife is beautiful!
HAPPYI'm smiling from ear to ear!
SADI feel so down.
SADEverything seems hopeless.
SADI'm heartbroken.
SADI can't stop crying
SADIt feels like the world is crashing down on me.
🚀 Given that our dataset lacks an adequate number of data samples for each classification, we're primarily assessing the viability of text classification within a no-code environment. Now, let's input the following text into the text area: I'm crying. Typically, in real-world no-code apps, you'd link this text area to dynamic data from another block in your scenario. However, for this demonstration, we're exclusively concentrating on testing text classification capabilities.
💥 Feel free to experiment with other phrases like 'I feel bad' or 'I feel great', but remember that our model's training data is limited (we usually need more data to get different moods detected), so it may not accurately detect the correct emotion. In a real-life situation, we’d tackle this by adding more examples of data for different emotions. This helps our model get better at recognizing and understanding a wider range of feelings.
🌠 For the form configuration, we'll utilize a random text input. Then, we'll link our text data field to the AI block, as depicted below:
🌟 Let's proceed to save and run our no-code app. Upon doing so, we'll observe that the system detects the phrase 'I feel great' as 'HAPPY'.
This example shows how Text Classification can boost no-code apps. It simplifies tasks across different areas. For example, in customer service, it automatically sorts and routes incoming queries, making communication more efficient. In content moderation, it helps detect and remove inappropriate content, keeping online spaces safer. And in sentiment analysis, it lets organizations quickly understand public opinion or customer feedback, allowing for quick responses or making the most of positive feedback. Overall, Text Classification in no-code AI is a powerful tool for handling various challenges efficiently.

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