TweetLex: Unveiling Structural Features Through Lexicon-Based Analysis of Twitter Data

The fundamental building blocks of any text lie in its structural features, namely the words employed by social media users. When examined in an objective and factual manner, these words can unlock meaningful information. Written expressions provide valuable insights into the underlying intentions of individuals. Researchers have diligently delved into this domain, exploring a multitude of applications. Among them, e-commerce stands out as a revenue-generating field. Presently, a substantial portion of analysis efforts revolves around business analytics. This research introduces an algorithm designed to scrutinize social media users’ text (termed “Structural Features”) with the aim of detecting behavioral changes. Leveraging a lexicon-based approach, the algorithm identifies pertinent words and computes sentiment scores. Through this proposed methodology, a comprehensive understanding of user behavior emerges.