This project delves deep into the dynamics of the "ChatGPT" subreddit, utilizing a vast dataset scraped from Reddit, encompassing all posts and comments within the said subreddit. The study meticulously examines various aspects of the data through a multi-pronged analytical approach:
The initial step was to unpack the overarching trends within the subreddit. This involved a trend analysis, illustrating the distribution of posts across different timeframes - be it monthly, daily, or other specified intervals. Such analysis offers a temporal perspective on the ebb and flow of discussions related to ChatGPT.
To understand the vocabulary and recurrent themes, a word frequency analysis was conducted. This gave insight into the most commonly used words, reflecting the primary subjects of interest and buzzwords in the ChatGPT discussions.
A critical component of the study was to gauge the sentiment of the community towards ChatGPT. This involved assessing the sentiment distribution in both post titles and comments. Furthermore, the project spotlighted the most prevalent words associated with each sentiment label, providing a nuanced understanding of positive, negative, and neutral sentiments.
To further deepen the understanding of the subreddit's content, Latent Dirichlet Allocation (LDA) topic modeling was employed. This technique unraveled the underlying themes and areas of interest dominating the ChatGPT domain, offering a thematic categorization of discussions.
In essence, this project presents a holistic view of the "ChatGPT" subreddit, weaving together insights from various analyses to paint a comprehensive picture of the community's interactions, interests, and sentiments.