South Korea’s deputy PM says AI wealth must benefit the public. The Samsung strike showed why.

Introduction

In South Korea's rapidly evolving AI landscape, understanding how to work with artificial intelligence systems is becoming increasingly crucial. This tutorial will guide you through building a simple AI-powered sentiment analysis tool using Python and the Hugging Face Transformers library. This type of system is similar to what companies like Samsung might use to analyze customer feedback or employee sentiment during labor negotiations. By the end of this tutorial, you'll have a working application that can process text and determine whether it expresses positive, negative, or neutral sentiment.

Prerequisites

Before beginning this tutorial, you should have:

• Basic Python programming knowledge
• Python 3.7 or higher installed on your system
• Basic understanding of machine learning concepts
• Access to a command line or terminal

Step-by-Step Instructions

1. Set Up Your Development Environment

First, create a new directory for your project and set up a virtual environment to keep your dependencies isolated:

mkdir ai-sentiment-analyzer
 cd ai-sentiment-analyzer
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

This step ensures that you don't interfere with other Python projects on your system and that all dependencies are properly managed.

2. Install Required Libraries

Next, install the necessary Python libraries for working with AI models:

pip install transformers torch

The transformers library provides pre-trained models for various NLP tasks, while torch is the deep learning framework that powers these models.

3. Create the Main Analysis Script

Create a new file called sentiment_analyzer.py and add the following code:

from transformers import pipeline

def analyze_sentiment(text):
    # Initialize the sentiment analysis pipeline
    sentiment_pipeline = pipeline("sentiment-analysis")
    
    # Analyze the sentiment
    result = sentiment_pipeline(text)
    
    return result

if __name__ == "__main__":
    # Example text for analysis
    sample_text = "Samsung Electronics is facing labor tensions as AI technology transforms the workforce."
    
    # Analyze sentiment
    sentiment = analyze_sentiment(sample_text)
    
    print(f"Text: {sample_text}")
    print(f"Sentiment: {sentiment[0]['label']}")
    print(f"Confidence: {sentiment[0]['score']:.4f}")

This code initializes a pre-trained sentiment analysis model and demonstrates how to use it to analyze text. The model will classify the text as either POSITIVE or NEGATIVE.

4. Run the Basic Analysis

Execute your script to see how the sentiment analyzer works:

python sentiment_analyzer.py

You should see output similar to:

Text: Samsung Electronics is facing labor tensions as AI technology transforms the workforce.
Sentiment: NEGATIVE
Confidence: 0.9876

The model correctly identified the text as negative with high confidence, which aligns with the context of labor tensions mentioned in the news article.

5. Enhance the Analyzer with Multiple Text Support

Update your script to handle multiple texts at once:

from transformers import pipeline

def analyze_sentiment(texts):
    # Initialize the sentiment analysis pipeline
    sentiment_pipeline = pipeline("sentiment-analysis")
    
    # Analyze sentiment for multiple texts
    results = sentiment_pipeline(texts)
    
    return results

if __name__ == "__main__":
    # Multiple sample texts
    sample_texts = [
        "Samsung Electronics is facing labor tensions as AI technology transforms the workforce.",
        "AI technology is revolutionizing the manufacturing industry.",
        "Employees are concerned about job security with AI implementation."
    ]
    
    # Analyze sentiment for all texts
    sentiments = analyze_sentiment(sample_texts)
    
    for i, (text, result) in enumerate(zip(sample_texts, sentiments)):
        print(f"Text {i+1}: {text}")
        print(f"Sentiment: {result['label']}")
        print(f"Confidence: {result['score']:.4f}")
        print("-" * 50)

This enhancement allows you to process multiple pieces of text simultaneously, which is useful for analyzing large datasets of employee feedback or public sentiment during labor negotiations.

6. Add Custom Text Input Functionality

Modify your script to accept user input:

from transformers import pipeline

def analyze_sentiment(text):
    # Initialize the sentiment analysis pipeline
    sentiment_pipeline = pipeline("sentiment-analysis")
    
    # Analyze the sentiment
    result = sentiment_pipeline(text)
    
    return result

if __name__ == "__main__":
    print("AI Sentiment Analyzer for Labor Tensions Analysis")
    print("Enter text to analyze (type 'quit' to exit):")
    
    while True:
        user_input = input("\nEnter text: ")
        
        if user_input.lower() == 'quit':
            break
        
        # Analyze sentiment
        sentiment = analyze_sentiment(user_input)
        
        print(f"Sentiment: {sentiment[0]['label']}")
        print(f"Confidence: {sentiment[0]['score']:.4f}")

This interactive version allows you to test various inputs, simulating how a company might analyze employee feedback or public statements during labor disputes.

7. Save Results to a File

Update your script to save analysis results to a CSV file for further processing:

import csv
from transformers import pipeline

def analyze_sentiment(text):
    sentiment_pipeline = pipeline("sentiment-analysis")
    result = sentiment_pipeline(text)
    return result

def save_results_to_csv(results, filename="sentiment_analysis.csv"):
    with open(filename, 'w', newline='', encoding='utf-8') as csvfile:
        fieldnames = ['text', 'sentiment', 'confidence']
        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
        
        writer.writeheader()
        for result in results:
            writer.writerow({
                'text': result['text'],
                'sentiment': result['label'],
                'confidence': result['score']
            })

if __name__ == "__main__":
    sample_texts = [
        "Samsung Electronics is facing labor tensions as AI technology transforms the workforce.",
        "AI technology is revolutionizing the manufacturing industry.",
        "Employees are concerned about job security with AI implementation."
    ]
    
    # Analyze all texts
    results = []
    for text in sample_texts:
        sentiment = analyze_sentiment(text)
        results.append({
            'text': text,
            'label': sentiment[0]['label'],
            'score': sentiment[0]['score']
        })
    
    # Save results
    save_results_to_csv(results)
    print("Analysis results saved to sentiment_analysis.csv")

This final enhancement demonstrates how AI sentiment analysis tools can be integrated into business processes, helping companies understand public sentiment and employee concerns during major transitions like AI implementation.

Summary

In this tutorial, you've learned how to build an AI-powered sentiment analysis tool using the Hugging Face Transformers library. This system can help analyze public sentiment and employee concerns during labor negotiations, similar to what South Korea's Deputy Prime Minister Bae Kyung-hoon warned about in the context of AI's impact on society. The tool you've created can process text, determine sentiment, and save results for further analysis. This approach is valuable for understanding how AI technologies can be used to address social concerns and ensure that AI wealth benefits the public, as highlighted in the news article.