• Exploratory Data Analysis and Sentiment Analysis on Brazilian E-Commerce Website

      Andriamanalimanana, Bruno; Committee Chair; Novillo, Jorge; Thesis Committee; Reale, Michael; Thesis Committee; Patel, Mihir (2020-05)
      In the past few years, the growth of e-commerce and digital marketing has generated a huge volume of opinionated data. Analyzing those data would provide enterprises with insight for better business decisions. E-commerce web applications are almost ubiquitous in our day to day life, however as useful as they are, most of them have little to no adaptation to user needs, which in turn can cause both lower conversion rates as well as unsatisfied customers. We propose a machine learning system which learns the user behavior from multiple previous sessions and predicts useful metrics for the current session. In turn, these metrics can be used by the applications to customize and better target the customer, which can mean anything from offering better offers of specific products, targeted notifications or placing smart ads. With recent advances in every field, the need for developing efficient techniques for analytics as well as predictions have increased to larger extend. As the data gets large it becomes difficult for companies to handle such large volume of data, therefore new approaches are developed. Here we work with the dataset from olist e-commerce website taken from year 2016 to 2018. In this work, we study sentiment analysis of product reviews in Portuguese since this dataset contains data from Brazilian supermarkets. Understanding customer sentiments is of paramount importance in marketing strategies today. Not only will it give companies an insight as to how customers perceive their products and/or services, but it will also give them an idea on how to improve their offers. This project attempts to understand the correlation of different variables in customer reviews e-commerce products, and to classify each review whether it recommends the reviewed product or not and whether it consists of positive, negative, or neutral sentiment.