Welcome to our comprehensive tutorial on IMDb movie review analysis using Python and LSTM (Long Short-Term Memory) networks! In this video, we’ll walk you through the entire process of performing sentiment analysis on movie reviews, from data preprocessing to building and training the LSTM model.
What You'll Learn:
🔹 Introduction to Sentiment Analysis: Understand the basics of sentiment analysis and its importance in natural language processing (NLP).
🔹 Data Collection: Learn how to collect and preprocess IMDb movie review data for analysis.
🔹 Text Preprocessing: Techniques for cleaning and preparing text data, including tokenization and padding sequences.
🔹 Building an LSTM Model: Step-by-step guide to building an LSTM network using TensorFlow and Keras.
🔹 Training and Evaluation: Train your LSTM model and evaluate its performance on sentiment classification tasks.
🔹 Visualizing Results: Tips for visualizing model performance and interpreting the results.
Tools and Libraries Used:
Python: The programming language used for scripting and model development.
Pandas: For data manipulation and analysis.
NumPy: For numerical operations.
TensorFlow & Keras: To build and train the LSTM network.
NLTK & SpaCy: For text preprocessing tasks.
Matplotlib & Seaborn: For visualizing the data and results.
🔗 Useful Links & Resources:
Dataset Source: [ Ссылка ]
Code Repository: [ Ссылка ]
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Additional Resources:
TensorFlow Documentation
Keras Documentation
IMDb Dataset
Who Is This For?
This tutorial is ideal for beginners to intermediate learners who have a basic understanding of Python and machine learning concepts. Whether you are a data science enthusiast, a student, or a professional looking to enhance your skills in NLP and sentiment analysis, this video is for you!
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Thank you for watching, and happy coding!
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