Topic Modelling on G2 Customer Reviews with LLMs (Full guide) скачать в хорошем качестве

Topic Modelling on G2 Customer Reviews with LLMs (Full guide)

🚀 Dive into the world of advanced review analysis with our latest video! We're tapping into the potential of OpenAI's LLMs to transform the way you understand customer feedback. Whether you're sifting through G2 reviews, support tickets, product ideas, or even YouTube comments, our step-by-step guide has you covered. 📊 Designed for Everyone This video is perfect for data scientists, product managers, or anyone intrigued by AI's role in review analysis. It's tailored to equip you with the skills needed to effectively harness the power of customer opinions. 🔍 What You'll Get From the video: • Topic Modelling Techniques: Uncover the hidden themes in customer feedback. See how LLMs like ChatGPT enable new forms of analysis. • Practical Walkthrough: Follow along with my high-level as well as detailed code walkthrough and apply these skills in real-time. 💻 Full Code Included: Don't just watch – implement! We provide you with the full code to try out these techniques yourself. Github Repo: https://github.com/balmasi/g2_reviews... Want to leverage AI for your business? Book a discovery call: https://cal.com/borna/discovery 00:00 - Teaser of what we'll be building 00:42 - Concept: Overview of application of these techniques 01:43 - Concept: Downloading review data with Apify 02:10 - Concept: Splitting reviews 03:03 - Concept: Categorizing sentiment (Likes/Dislikes) 04:20 - Concept: Embeddings 06:19 - Concept: Clustering 07:00 - Concept: Naming clusters with LLMs 08:16 - Concept: Visualizing the reviews 09:11 - Web app demo 12:30 - Code: Downloading the project 13:39 - Customizing the G2 Data - Getting started with Apify 14:52 - Running the G2 scraper 17:27 - Code: Preprocessing the data 18:10 - Code: Splitting the reviews to sentences 18:37 - Code: Embedding the sentences 19:07 - Code: Clustering the embeddings 19:50 - Code: Getting LLMs to extract the topic of each cluster 22:44 - Code: Dimensionality reduction with UMAP for visualization 23:38 - Closing comments Connect on LinkedIn:   / balmasi