Movie Review Sentiment Analysis Project Part-10 | Building Custom Embedding Layer using Word2Vec
In this video, we continue our end-to-end Movie Review Sentiment Analysis project series.
Previously, we trained a Word2Vec embedding model using Gensim and now we finally start using it inside our deep learning model 🚀
This video focuses on converting raw text into meaningful dense vectors and integrating them into a neural network.
What you’ll learn in this video:
Creating a Text Vectorization Layer
Building the vocabulary from dataset
Constructing the embedding matrix from trained Word2Vec model
Mapping words → vectors
Building a custom Keras Embedding Layer using the pretrained embeddings
Instead of relying on randomly initialized embeddings, we are now leveraging semantic understanding learned by Word2Vec — a major step toward Version 2 of this project.
💡 By the end of this video, our model will understand similarity between words like “good”, “great”, and “excellent” instead of treating them as unrelated tokens.
🔗 Previous Video (Word2Vec Training):
• Movie Review Sentiment Analysis Project Pa...
🔗 IMDB Dataset:
https://www.kaggle.com/datasets/laksh...
🔗 Project Resources:
https://drive.google.com/drive/folder...
🔗 Github Link:
https://github.com/animesh1012/machin...
🔗 Full Playlist:
• Movie Review Sentiment Analysis Project fr...
-----------Content of Video----------------
00:00 - Recap
00:27 - Train & Test Split
03:36 - Text Vectorization
11:00 - Building Custom Embedding
24:57 - Summary
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#NLP #Word2Vec #SentimentAnalysis #DeepLearning #TensorFlow #Gensim #MachineLearning #DataScience #Python #aiprojects
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