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Study mode:
on
1
Intro
2
What we will build
3
Code links and prerequisites
4
Dataset download and preprocessing
5
Using RoBERTa sentiment analysis model
6
Retriever model for building dense vectors
7
Create Pinecone vector index
8
Sentiment scores, vectors, and indexing
9
Sentiment analysis / opinion mining
10
Sentiment analysis with specific date range
11
Sentiment analysis on specific info
12
Final notes
Description:
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.

Advanced Sentiment Analysis with NLP Transformers and Vector Search

James Briggs
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