Advanced Sentiment Analysis with NLP Transformers and Vector Search

Explore advanced sentiment analysis techniques using NLP transformers and vector search in this comprehensive tutorial. Learn to apply sentiment analysis to large datasets, creating insightful query databases for the hotel industry. Generate sentiment labels and scores from customer reviews, store them in a Pinecone index as metadata alongside text vectors, and query the index to understand customer opinions. Follow along as the video guides you through dataset preprocessing, using the RoBERTa sentiment analysis model, building dense vectors with a retriever model, creating a Pinecone vector index, and performing various sentiment analyses, including specific date ranges and targeted information. Gain valuable skills in machine learning, deep learning, and AI while using Python to extract meaningful insights from customer feedback.

Intro
What we will build
Code links and prerequisites
Dataset download and preprocessing
Using RoBERTa sentiment analysis model
Retriever model for building dense vectors
Create Pinecone vector index
Sentiment scores, vectors, and indexing
Sentiment analysis / opinion mining
Sentiment analysis with specific date range
Sentiment analysis on specific info
Final notes