Vocabulary Building for Sentiment Classification using RNN in NLP with Python

Course link: https://aicourse.thinkific.com/courses/nlp-natural-language-processing-in-python-for-beginners
00:00 – Vocabulary Implementation
09:48 – Vocabulary Implementation Helpers
15:34 – Vocabulary Implementation From File
In this video we focus on creating a custom vocabulary from raw text data to prepare it for sentiment analysis using Recurrent Neural Networks (RNN). Using a dataset of Yelp restaurant reviews, we demonstrate how to extract reviews, handle variable-length input, and convert words into indexed tokens. The video includes step-by-step implementation of helper functions such as add_token, lookup_token, and lookup_index, along with building the vocabulary directly from a DataFrame using frequency cutoffs. All code is written in Python with support from libraries like PyTorch, NumPy, and Pandas. This foundational step is essential in Natural Language Processing (NLP) projects, especially when preparing text data for deep learning models like RNNs. Whether you're a beginner or looking to strengthen your NLP pipeline, this tutorial will give you a clear understanding of how vocabulary is built and managed in real-world machine learning tasks.

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Видео Vocabulary Building for Sentiment Classification using RNN in NLP with Python канала AI Sciences