  After the COVID-19 pandemic caused internet usage to grow by 70%, there has
been an increased number of people all across the world using social media.
Applications like Twitter, Meta Threads, YouTube, and Reddit have become
increasingly pervasive, leaving almost no digital space where public opinion is
not expressed. This paper investigates sentiment and semantic relationships
among comments across various social media platforms, as well as discusses the
importance of shared opinions across these different media platforms, using
word embeddings to analyze components in sentences and documents. It allows
researchers, politicians, and business representatives to trace a path of
shared sentiment among users across the world. This research paper presents
multiple approaches that measure the relatedness of text extracted from user
comments on these popular online platforms. By leveraging embeddings, which
capture semantic relationships between words and help analyze sentiments across
the web, we can uncover connections regarding public opinion as a whole. The
study utilizes pre-existing datasets from YouTube, Reddit, Twitter, and more.
We made use of popular natural language processing models like Bidirectional
Encoder Representations from Transformers (BERT) to analyze sentiments and
explore relationships between comment embeddings. Additionally, we aim to
utilize clustering and Kl-divergence to find semantic relationships within
these comment embeddings across various social media platforms. Our analysis
will enable a deeper understanding of the interconnectedness of online comments
and will investigate the notion of the internet functioning as a large
interconnected brain.
