Sentiment analysis is a powerful NLP technique used to determine the emotional tone behind words. Here's a quick guide:

What is Sentiment Analysis?

  • Definition: Extracting subjective information from text (e.g., positive, negative, neutral)
  • Use Cases: Social media monitoring, product reviews, customer feedback
  • Core Concepts:
    • Text preprocessing
    • Feature extraction (Bag-of-Words, TF-IDF)
    • Machine learning models

Tools & Libraries

  • Python: NLTK, TextBlob, Transformers
  • Frameworks: TensorFlow, PyTorch
  • Cloud Services: AWS Comprehend, Google Cloud Natural Language API

Implementation Steps

  1. Data Collection: Gather labeled datasets (e.g., IMDb reviews)
  2. Model Training: Use pre-trained models like BERT for better accuracy
  3. Evaluation: Measure performance with metrics like F1-score
Sentiment Analysis Example

For deeper insights, check our NLP Introduction Tutorial to understand related concepts. 📈