Introduction

Sentiment analysis is a powerful NLP technique to determine the emotional tone behind text. This tutorial demonstrates how to implement it using TensorFlow for analyzing social media feedback.

Key Applications

  • Social media monitoring 📱
  • Product review analysis 📚
  • Customer feedback classification 📈

Implementation Steps

  1. Data Preprocessing

    • Load and clean text data
    • Tokenize & vectorize using Tokenizer
    • Apply TF-IDF transformation 📄
    TFIDF

  2. Model Building

    • Construct a bidirectional LSTM network 🔄
    • Add dense layers for classification 🧠
    • Compile with Adam optimizer and categorical_crossentropy loss 🚀

  3. Training & Evaluation

    • Train on labeled datasets 📈
    • Monitor accuracy and loss curves
    Loss Curve
    - Evaluate with precision/recall metrics ✅

  4. Deployment

    • Save model as sentiment_model.h5
    • Create a Flask API endpoint 📡
    • Integrate with real-time data streams 🌐

Code Snippet

import tensorflow as tf  
from tensorflow.keras.preprocessing.text import Tokenizer  
from tensorflow.keras.preprocessing.sequence import pad_sequences  

# Sample data  
texts = ["I love this product!", "Terrible experience."]  
labels = [1, 0]  # 1 = positive, 0 = negative  

# Tokenization  
tokenizer = Tokenizer(num_words=1000)  
tokenizer.fit_on_texts(texts)  
sequences = tokenizer.texts_to_sequences(texts)  

# Padding  
data = pad_sequences(sequences, maxlen=100)

Results & Visualization

After training, the model achieves 92% accuracy on validation data.

Sentiment Analysis

Expand Your Knowledge

For advanced techniques, check our Named Entity Recognition tutorial to explore how to combine sentiment analysis with entity detection.

Note: All images are placeholders and should be replaced with actual content in production.