Emotion Analysis in Gasoline Consumption in Mexico using Machine Learning

Emotions and Sentiment Analysis has had an important role in increasing business benefits on commerce sector. Emotions Analysis as well as Sentiment Analysis is a common machine learning technique used to analyze opinions of people about certain company aspects such as products image, product consumption, marketing campaigns, client's preferences and social or political movements. The relevance of Emotions Analysis research lies in the enormous economic impact that it provides to enterprises. In this work, we present an emotion analysis to obtain the principal feature set related to the emotions that make consumers prefer a gas station over others. Our approach, to understand gasoline consumption behavior in Mexico, is based on machine learning and statistical analysis. We use a conventional statistical approach to analyze the characteristics of gas stations preferred by customers based on their emotions. Finally, supervised Machine Learning classification methods are applied in order to predict the probability that a gas station is selected on the basis of customers' emotions.