TOPIE: An open-source opinion mining pipeline to analyze consumers’ sentiment in Brazilian Portuguese

Abstract

The growth of social media and user-generated content (UGC) on the Internet provides a huge quantity of information that allows discovering the experiences, opinions, and feelings of users or customers. These electronic Word of Mouth statements expressed on the web are prevalent in business and service industry to enable a customer to share his/her point of view. However, it is impossible for humans to fully understand it in a reasonable amount of time. Opinion mining (also known as Sentiment Analysis) is a sub-field of text mining in which the main task is to extract opinions from UGC. Thus, this work presents an open source pipeline to analyze the costumer’s opinion or sentiment in Twitter about products and services offered by Brazilian companies. The pipeline is based on General Architecture for Text Engineering (GATE) framework and the proposed hybrid method combines lexicon-based, supervised learning, and rulebased approaches. Case studies performed on Twitter real data achieved precision of almost 70%.