Machine Learning for Feeling Analysis in Twitter Communications: A Case Study in HEYDRU!, Perú

Abstract

Nowadays, feeling analysis has become a trend; above all, in digital product development companies, as it is essential for rapid and automatic analysis. Feeling analysis deals with emotions with the help of software, and it is playing an unavoidable role in workplaces. The constant growth of social networks, especially the Twitter social network, has made the ability to understand and comprehend users or clients take a greater scope regarding their needs; and therefore, increase the complexity of analysis of this social network, causing excessive expenses in time, personnel, and money. This work presents a solution through the application of Machine Learning (ML) for feeling analysis and thus improve analysis, execution time and customer satisfaction. The scope of this research is limited to using the Support Vector Machine (SVM), a supervised learning technique for the intended analysis. The model derives from the ML technique making use of cross validation. CRISP-ML(Q) is the applied Methodology. The results show that the use of ML allows efficient feeling analysis in Twitter communications.