The power of sentiment: big data analytics meets machine learning for emotional insights

The integration of Big Data and Machine Learning actually helped to transform industries throughout the global market. Among all sorts of enhancements, the one that is particularly stimulating in this field is the process of identifying emotions in large data volumes, known as Sentiment Analysis. The strength of sentiment analysis, therefore, hides inthe ability to capture the feelings, behavior and sentiments of the public, which can go a long way in helping different sectors, including marketing, politics, customer service, and health, among others.This paper also assesses the application of Big Data analytics in realizing emotional patterns supported by machine learning techniques. Using NLP on social media, reviews, and other textual data gives the business insights into the consumer’s emotions, allowing them to customize their offerings better. The paper also discusses topical issues and issues encompassing these systems; noise, multiple languages and that the structures are human emotional. The paper aims to compare the machine-learning models such as the SVM, Random Forests and neural networks in their efficiency to interpret sentiments. Also, we look at the future of sentiment analysis and how it will influence the interaction between humans and machines, markets, and decision-making.