Consumer Sentiment Analysis Using Twitter

Abstract

Sentiment analysis is the task of finding people’s opinions about specific objects/matters. Ordinary people’s opinions affect the decision-making process. Today, there is a massive explosion of “sentiments” available on social media, e.g. Twitter. Twitter is one of the most popular worldwide social-networking services. Twitter is a widely-used way to get people’s opinion about some topics when you read their posts. In this study, a model was coded in R environment and implemented and tested using a large dataset to estimate people’s opinions concerning specific topics that can be used for implementing better decision in market research. To achieve this goal, a large set of Twitter data along with a reference to a specific business was captured and fed to two models namely, Naïve Bayes and Support Vector Machine (SVM) to classify them. Then, I obtained the result at identifying percentage of positive and negative opinions towards the specific business.