All videos
0:00 / 0:00
research

Vocabulary Building for Sentiment Classification using RNN in NLP with Python

AI Sciences16 June 2026Watch on YouTube

What you'll learn

  • Learn how to build a custom vocabulary from raw text data for sentiment analysis using RNNs
  • Understand practical implementation of helper functions like add_token, lookup_token, and lookup_index in Python
  • See how to tokenize words from reviews and convert them to indexed tokens with frequency cutoffs
  • Discover how to handle variable-length input from Yelp reviews and organize them in a DataFrame

Frequently asked questions

What is the purpose of vocabulary construction in NLP and sentiment analysis?
Vocabulary construction converts raw text data into a structured format suitable for machine learning models. This includes extracting words, creating indices, and handling word frequencies, which is essential for training RNNs on sentiment analysis tasks.
Which helper functions are implemented in this video for vocabulary management?
The video demonstrates implementation of add_token (adding words to vocabulary), lookup_token (looking up words), and lookup_index (looking up indices). These functions enable efficient management and conversion between text terms and numeric indices.
How are word frequencies used when building the vocabulary?
Frequency cutoffs filter out words that appear too infrequently in the dataset. This helps reduce noise and focus on significant words that are more relevant for sentiment analysis.
Which Python libraries and datasets are used in this tutorial?
The tutorial uses PyTorch, NumPy, and Pandas along with a dataset of Yelp restaurant reviews. These tools support data processing, tokenization, and preparation for deep learning models.

Topics

nlprnnsentiment-analysispython-implementation

Description from the channel

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. ✅ Stay Connected to Us. 👉 Twitter: https://twitter.com/AISciencesLearn 👉 Facebook: https://www.facebook.com/AISciencesLearn 👉 LinkedIn: http://www.linkedin.com/company/ai-sciences 👉 Website: http://www.aisciences.io ✅ For Business Inquires: contact@aisciences.io ============================= ✅ Important Python Projects: 👉 Get a look at our course on data science and AI here: https://bit.ly/3thtoUJ 👉 The Python Codes are available at this link: https://www.aisciences.academy/ytube-cfd ✅ Recommended Playlists: 👉 Artificial Intelligence https://www.youtube.com/playlist?list=PLVgEzPHodXi0tO_iLwoZcEXiFPekTZlyj 👉 Machine Learning https://www.youtube.com/playlist?list=PLVgEzPHodXi01AWPo06GOsM-ba_e1PJ1l ✅ Other Videos You Might Be Interested In Watching: 👉 Simplest Password Cracker Using Python | Tips and Tricks | Most Easy https://www.youtube.com/watch?v=EmwmbahsM_E 👉 Build Facebook's Wav2Vec2 Model For Speech To Text Application | Easy Python Tutorial https://www.youtube.com/watch?v=t_qDAqUfqhY 👉 Fake Currency Detection Using Logistic Regression Model | Python | Tutorial For Beginners https://www.youtube.com/watch?v=dJO69JzldmQ 👉 Question Answering Bot | AI Based | with Hugging Face Transformers | Python https://www.youtube.com/watch?v=s4rV6Wyp23A ============================= ✅ About AI Sciences. AI Sciences is an e-learning company; the company publishes online courses and books about data science and computer technology for anyone, anywhere. We are a group of experts, Ph.D. students, and young practitioners of artificial intelligence, computer science, machine learning, and statistics. Some of us work for big-name companies like Google, Facebook, Microsoft, KPMG, BCG, and Mazars. We decided to produce courses and books mainly dedicated to beginners and newcomers on the techniques and methods of machine learning, statistics, artificial intelligence, and data science. Initially, our objective was to help only those who wish to understand these techniques more easily and to be able to start with less theory or lengthy reading. Today, we publish more complete books on selected topics for a wider audience. 🔔 Subscribe To our Channel For More Content of Data Science, Machine Learning, and AI: https://www.youtube.com/@AISciencesLearn ================================= #predictionofsocialmediafollowers #socialmediafollowersbymachinelearning #socialmediamarketing #socialmediamarketingforbeginners Disclaimer: We do not accept any liability for any loss or damage which is incurred by you acting or not acting as a result of watching any of my publications. You acknowledge that you use the information I provide at your own risk. Do your own research. Copyright Notice: This video and my YouTube channel contain dialog, music, and images that are the property of AI Sciences. You are authorized to share the video link and channel and embed this video in your website or others as long as a link back to my Youtube Channel is provided. © AI Sciences