Sentiment Analysis within Big Data Analytics A First Step Toward Understanding, Representing, and handling Class Imbalance

The unprecedented growth of big data, driven in large part by the continuous stream of user-generated content on social media platforms, has made the automatic analysis of public sentiment an increasingly indispensable capability. By extracting and interpreting the opinions, emotions, and attitudes embedded in this content, sentiment analysis enables organizations and decision-makers to derive timely, large-scale, and nuanced insight from data that would otherwise be too vast and unstructured to interpret manually, positioning it as a central and rapidly growing subfield of big data analytics. Realizing this potential in practice, however, is complicated by the highly skewed sentiment-class distributions typically observed in real-world datasets, where minority sentiments are frequently underrepresented and, as a result, overlooked by conventional classification approaches.
ISBN -N0
978-9969-07-396-6