6 Advanced text mining with generative AI

In the previous chapter, you got a taste of text-mining basics and discovered how generative AI can speed up and refine your analyses. Now, let’s go deeper. Ahead, you’ll tackle advanced NLP techniques such as sentiment analysis and text summarization. These tools are invaluable in the business world, enabling companies to swiftly gauge customer sentiment from reviews, social media, or customer service interactions, leading to more informed decision-making. Text summarization, on the other hand, can distill lengthy reports, research findings, or customer feedback into digestible insights, saving your precious time and ensuring key information doesn’t go unnoticed. Together, these techniques can significantly enhance how businesses understand and respond to their audiences, driving better strategies and outcomes.

6.1 Review analysis

6.2 Sentiment analysis

6.2.1 What can you learn from sentiment analysis?

6.2.2 Direct sentiment analysis with generative AIs

6.2.3 Sentiment analysis with generative AI’s API

6.2.4 Sentiment analysis with machine learning

6.2.5 Sentiment analysis with a suboptimal model

6.2.6 Sentiment analysis on translated inputs

6.2.7 Sentiment analysis with multilingual models

6.2.8 Sentiment analysis with zero-shot learning models

6.2.9 Comparing results of advanced sentiment analysis

6.3 Text summarization

6.3.1 How can you benefit from text summarization?

6.3.2 How can generative AI help in text summarization?

6.3.3 Summarizing text with ChatGPT

6.3.4 Summarizing text with dedicated libraries

6.3.5 Topic modeling

Summary