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After the COVID-19 pandemic caused internet usage to grow by 70%, there hasbeen an increased number of people all across the world using social media.Applications like Twitter, Meta Threads, YouTube, and Reddit have becomeincreasingly pervasive, leaving almost no digital space where public opinion isnot expressed. This paper investigates sentiment and semantic relationshipsamong comments across various social media platforms, as well as discusses theimportance of shared opinions across these different media platforms, usingword embeddings to analyze components in sentences and documents. It allowsresearchers, politicians, and business representatives to trace a path ofshared sentiment among users across the world. This research paper presentsmultiple approaches that measure the relatedness of text extracted from usercomments on these popular online platforms. By leveraging embeddings, whichcapture semantic relationships between words and help analyze sentiments acrossthe web, we can uncover connections regarding public opinion as a whole. Thestudy utilizes pre-existing datasets from YouTube, Reddit, Twitter, and more.We made use of popular natural language processing models like BidirectionalEncoder Representations from Transformers (BERT) to analyze sentiments andexplore relationships between comment embeddings. Additionally, we aim toutilize clustering and Kl-divergence to find semantic relationships withinthese comment embeddings across various social media platforms. Our analysiswill enable a deeper understanding of the interconnectedness of online commentsand will investigate the notion of the internet functioning as a largeinterconnected brain.

Exploring Embeddings for Measuring Text Relatedness: Unveiling Sentiments and Relationships in Online Comments