Performing Sentiment Analysis on Tweets: A Comparison of Machine Learning Algorithms Across Large Data Sets

Abstract

Sentiment Analysis is a popular topic in machine learning, a subfield of computer science. In the past, Sentiment Analysis has been widely adopted in e-commerce and helps organizations analyze customer satisfaction of products and services. More recently, Sentiment Analysis has expanded its applications across government agencies as well, whether it being to analyze potential human threats within social media or political influence in election campaigns. Even more generally, humans are simply curious about how other humans are feeling. Two major approaches to Sentiment Analysis include lexical semantic analysis and machine learning. In this thesis, I will combine different word embedding techniques and use machine learning to analyze sentiments across published tweets. The overall goal is to discover which approach to Sentiment Analysis offers better performance.