NLP-based Sentiment Analysis for Apple product reviews

The goal of sentiment analysis, a subfield of natural language processing (NLP), is to enable computers to comprehend written language without human intervention. Analyses usually undergo multiple iterations before yielding an outcome. Machine learning algorithms then provide a sentiment score to the retrieved keywords. A sentiment score quantifies the emotional component of a sentiment analysis system. As an analytical tool, it gives a relative sense of the emotions portrayed in language. In an era where social media is pervasive, decision-makers now give knowing public opinion top priority. This study performs a thorough analysis by comparing the viewpoints expressed in tweets about Apple, a well-known company in the tech industry. This research utilizes a dataset curated from Kaggle, which provides access to a wide variety of user-generated content. The study makes use of advanced Natural Language Processing (NLP) tools, especially the tidytext package and the NRC Sentiment Lexicon, to interpret the nuanced layers of sentiment expressed in these tweets. The comparative analysis between these two methodologies offers insights into the complex dynamics of public perception and provides a valuable perspective for decision-makers navigating the complex terrain of digital discourse.