Exploring Sentiment Analysis in Social Media: A Natural Language Processing Case Study

Abstract

Social media plays an integral role in our daily lives, influencing and reflecting global perspectives through the consumption and creation of content. Platforms like YouTube are incredibly active, with a constant influx of video uploads, views, and comments. While the YouTube app allows us to browse videos and comments, it offers only a limited glimpse into the interests and trends of others. Analysing this vast data pool, encompassing diverse language styles, presents a significant challenge. This article delves into the YouTube Data API and its application in Python for accessing raw data. The process involves data cleaning using advanced Natural Language Processing (NLP) techniques, harnessing Python-based machine learning to explore social media interactions, and automating the extraction of trends and influential factors. The journey towards trend analysis is meticulously detailed, featuring examples that leverage a variety of open-source Python tools.