UNVEILING INSIGHTS WITH TWITTER DATA: EXPLORING TRENDS, SENTIMENTS, AND PREDICTIONS THROUGH SOCIAL MEDIA MINING

Abstract

With the rapid rise of social media, Twitter has emerged as a valuable source of real-time data, offering insights into public opinion, sentiments, and trending topics. The unstructured and noisy nature of Twitter content—characterized by hashtags, mentions, abbreviations, and emoticons—poses significant challenges for traditional text processing techniques like tokenization and stemming, which often fall short in capturing the platform’s linguistic nuances. As the relevance of Twitter data grows in areas such as sentiment analysis, brand monitoring, and trend prediction, the need for an advanced and comprehensive pre-processing approach becomes crucial. Effective preprocessing helps filter out irrelevant information while preserving context, enabling machine learning algorithms to classify tweets more accurately. This research focuses on leveraging machine learning for Twitter data classification by introducing a robust pre-processing pipeline, ultimately contributing to improved accuracy, deeper understanding of public sentiment, and more informed decision-making for businesses and researchers alike