All In with AI: Unraveling Podcast Personalities with Tweet Clustering
Today, I’m sharing an intriguing exploration that combines AI technology with the social media landscape of the popular ‘All In Podcast’ hosts. This journey is an enlightening illustration of how I, despite having minimal machine learning experience, leveraged the remarkable power of large language models (LLMs) like OpenAI’s GPT-4.
Hypothesis & Setup
My adventure started with a simple hypothesis: Given I have little experience in building a machine learning model, if I harness the power of an LLM like Open AI GPT, then I should be able to create an application that provides unique and valuable insights. To put this hypothesis to the test, I embarked on a project involving the well known “All In Podcast” hosts and their Twitter musings (i.e. @chamath, @DavidSacks, @Jason, @friedberg).
The setup was straightforward: I collected the 25 most recent tweets from each host, forming a dataset that I hoped could provide a unique insight into their shared and disparate views. My goal was to cluster these tweets into similar groupings to unearth any meaningful patterns.
Harnessing the Power of GPT-4
With my dataset in place (Fig.1), the next phase was to utilize ChatGPT to obtain vectorized embeddings of this data.
It might sound like a daunting task for someone with limited Python or machine learning experience, but with ChatGPT as a guide, it became surprisingly manageable.
Now I was able to use my vectorized dataset (Fig.2) as an input for a clustering algorithm.
Clustering and Visualizing Results
Leveraging the help of GPT-4 I was guided to use K-means algorithm for clustering.
The results of the clustering algorithm were then plotted to visualize the data and help me derive some meaningful conclusions. Despite my limited dataset, the application was successful in clustering the tweets.
As seen in (Fig.3), while the clusters are somewhat inconclusive, they do hint at shared viewpoints between some hosts. Chamath Palihapitiya, David Sacks, and jason all seem to share similar ideas while Dave Friedberg seems to deviate. However, it’s important to note that with a larger dataset, I might have been able to draw more definitive conclusions. For example, you may be able to detect when Dave Friedberg begins to shift to align closer with jason.
Conclusion: A Triumph for AI and LLMs
Despite some uncertainty in the clustering results, the overarching takeaway from my experiment is clear: My hypothesis held true! With the assistance of an LLM like ChatGPT, I was indeed able to construct a machine learning model and provide preliminary insights into the hosts’ shared viewpoints.
This experiment also showcased the power of LLMs in accelerating the learning process. What may have taken me days to accomplish without an LLM was efficiently handled in a matter of a few hours with the help of ChatGPT.
So, here we are. We’ve seen how AI can help me dive into a dataset, cluster it, visualize it, and derive insights — all with minimal prior experience. This adventure serves as a testament to the power of AI and the exciting possibilities it holds for those ready to explore it. Stay curious, my fellow tech adventurers.
