Sentiment analysis is a powerful NLP technique used to determine the emotional tone behind words. It helps in understanding whether the sentiment is positive, negative, or neutral. 📊

Key Concepts

  • What is Sentiment Analysis?
    It involves processing text to identify and classify opinions, emotions, or attitudes.

    Sentiment Analysis Process

  • Applications

    • Social media monitoring 📱
    • Customer feedback analysis 📋
    • Market research 🔍
    • Chatbot development 🤖

Tools & Libraries

  • Python (using NLTK, TextBlob, or Transformers) 🐍
  • R (with tm and sentimentr packages) 📊
  • Online Platforms (like Google Cloud Natural Language API) ☁️

Steps to Implement

  1. Data Collection
  2. Text Preprocessing (tokenization, stopword removal)
  3. Feature Extraction (TF-IDF, word embeddings)
  4. Model Training (using SVM, Naive Bayes, or deep learning)
  5. Evaluation (accuracy, F1-score)

For a deeper dive into NLP fundamentals, check out our introduction to NLP tutorial.

Social Media Analysis

Explore more tools and techniques in our Text Classification guide.