Master Sentiment Analysis with CoreML & CreateML

Table of Contents

1. Introduction
2. Building a Model with Create ML
3. Importing the Model into Xcode
4. Testing the Model with CSV Data
5. Handling the Error and Renaming the Label Field
6. Training the Model and Checking Accuracy
7. Using the Model for Sentiment Analysis
8. Exporting the Model to Xcode
9. Setting up the UI in SwiftUI
10. Analyzing Sentiments with User Input
11. Improving the Model and Data
12. Classifying Text Fields with Precise Emotions
13. Building Models to Check Abuse Level
14. Conclusion

Introduction

In this video tutorial, we will be exploring sentiment analysis, which is a form of text classification. The goal is to build a model using Create ML and import it into Xcode for sentiment analysis using Core ML. We will start by preparing our data, train our model, and then analyze sentiments using user input. We will also discuss ways to improve the accuracy of the model and explore other potential applications.

Building a Model with Create ML

To begin, we will launch Create ML and create a project for text classification. We will provide a name and description for the project. Then, we will select our data, which in this case is a CSV file containing a list of tweets. However, we might encounter an error indicating that there is no label field in the input data. We will address this issue by renaming the field to "label" using Numbers application. However, we need to be cautious as editing the file using Numbers may change the file extension. To ensure compatibility, we will manually edit the CSV file in Terminal and update the header from "sentiment" to "label."

Importing the Model into Xcode

Once We Are satisfied with the performance of the model, we can export it as "sentiment.analyzer" and import it into our Xcode project. We will need to import SwiftUI and Core ML to start writing our ContentView. We will create two variables: "userInput" to get a STRING from the user for sentiment analysis and "labelPrediction" to output the result as an Emoji Unicode string. We will construct a VStack in our body view, including a title, a text field for user input, and a button to perform the analysis.

Testing the Model with CSV Data

Before proceeding with user input, we can test the model's classification accuracy with different strings. By inputting various strings and pressing enter, we can observe how well the model classifies the sentiment. While some strings yield accurate results, others may not perform as well due to the limitations of the validation accuracy. As the model approaches 100% accuracy, it becomes a reliable indicator of its performance.

Handling the Error and Renaming the Label Field

While working with the CSV file, we may encounter an error that prevents us from selecting the label field. We can resolve this issue by renaming the field from "sentiment" to "label." However, editing the file using the Numbers application may cause it to be saved with a .numbers extension. To maintain compatibility with Create ML, we need to ensure the file remains in the .CSV format.

Training the Model and Checking Accuracy

Once the data and label field are correctly set, we can proceed with training the model. We can choose from different algorithms available in the dropdown menu. After initiating the training process, we can observe the training and validation accuracy. While the training accuracy may be around 98%, the validation accuracy may vary. We can further enhance the accuracy by adding more data and optimizing the model.

Using the Model for Sentiment Analysis

With the model trained and validated, we can utilize it for sentiment analysis. By inputting strings and pressing a button, we can use the model to predict the sentiment and display it as an Emoji at the bottom of the screen. This feature allows us to assess the model's performance in real-time.

Exporting the Model to Xcode

When we are satisfied with the model's accuracy, we can export it as "sentiment.analyzer" and import it into our Xcode project. This step allows us to utilize the model for sentiment analysis within our app.

Setting up the UI in SwiftUI

To enhance the user experience, we will create a user interface using SwiftUI. We will design the ContentView to include a button, a text field for user input, and a label to display the sentiment analysis result. With the UI in place, we can proceed to implement the functionality for sentiment analysis.

Analyzing Sentiments with User Input

By implementing the "analyzeButtonTapped" function, we can load the model, input user-generated strings, and utilize the model to predict the sentiment. The function will also assign an Emoji Unicode string to the "labelPrediction" variable, allowing us to display the sentiment result to the user.

Improving the Model and Data

To enhance the accuracy of the model, we can incorporate more data into the training process. Additionally, we can classify text fields with precise emotions, such as annoyance, frustration, happiness, and excitement. By considering the level of positivity and negativity in a tweet or statement, the model can provide more detailed sentiment analysis.

Classifying Text Fields with Precise Emotions

Expanding on the previous Notion, we can build models that classify text fields with precise emotions. This approach allows us to accurately analyze sentiments beyond just positive, negative, or neutral, providing a more nuanced understanding of user sentiments.

Building Models to Check Abuse Level

Given the prevalence of online abuse, it is essential to develop models that assess the level of abuse in user comments. While most platforms employ filters, some abusive comments can slip through. By building models specifically designed to identify and classify abusive language, we can enhance content moderation and create safer digital environments.

Conclusion

In this tutorial, we explored sentiment analysis using Create ML and Core ML in Xcode. We learned how to build a model, import it into our project, and utilize it for sentiment analysis with user input. We discussed strategies for improving the accuracy of the model, including adding more data and implementing more precise emotion classifications. Additionally, we explored the potential applications of building models to check the level of abuse in online comments. Utilizing machine learning in iOS development allows us to create intelligent and user-centric applications.