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AI326: Sentiment Analysis in Customer Feedback with AI

Training: Artificial Intelligence

Customer service and data analysis professionals learn techniques of sentiment analysis with language models. The course covers embeddings, zero-shot classification, and clustering, as well as their implementation with large language models such as OpenAI or Aleph Alpha. It addresses how customer feedback and social media content can be reliably analyzed and used for targeted actions.

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Possible course languages: German

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Agenda:

Introduction of fundamental concepts, such as sentiment analysis and specifics in the context of LLMs / embeddings:
- Learning what embeddings are and how they are applied in sentiment analysis

Usage of LLMs from OpenAI (embeddings), Aleph Alpha embeddings:
- Getting to know the tools and their application in sentiment analysis

Zero shot classification (no training data):
- Introduction to zero shot classification and its application when no training data is available

GPT classification (chat completion):
- Understanding how GPT classification works and how it can be applied for sentiment analysis in chat completion

Embedding visualization and dimensionality reduction:
- Learning what dimensionality reduction is and how it can be used to visualize embeddings

Clustering and classification:
- Introduction to clustering and building classifiers for sentiment analysis, understanding relationships and groups

Exploration of possible applications (dashboard, social media agent etc.):
- Exploring application possibilities of sentiment analysis in various areas, such as dashboards or social media agents

Objectives:

Understand various sentiment analysis techniques, deploy tools such as OpenAI and Aleph Alpha Embeddings, analyze customer feedback and social media content, apply sentiment analysis methods.

Target audience:

Developers
IT Professionals

Prerequisites:

To effectively follow the learning pace and content of the training AI322 Digital Customer Advisor with AI, we recommend prior participation in the following courses:

Alternatively, prior knowledge in the following areas is required:

Python programming fundamentals
Prompt Engineering
Large Language Models

Description:

In many enterprise contexts it is important to evaluate the sentiment of people. With sentiment analysis, positive or negative customer reviews, frustrated customers can be quickly and reliably detected and appropriate measures can be initiated.

In the workshop AI326 Sentiment Analysis in Customer Feedback with AI various techniques for sentiment analysis with language models are presented, including embeddings, zero shot classification, clustering. Participants learn how they can implement these techniques with Large Language Models (LLMs) from OpenAI or Aleph Alpha to analyze e.g. customer feedback or social media content. After completion of the course, participants will be able to understand and apply various sentiment analysis methods.

We recommend additional participation in the following content-related modules: