Twitter-based opinion mining for flight service utilizing machine learning

Abstract

Twitter is one of the most prominent social networking platforms so far. Millions of users utilize Twitter to share their thoughts and views on various topics of interest every day resulting a huge amount of data. This data could be considered to have a rich source of useful information hidden inside. Using machine learning to this data may give rise to effective recommender frameworks for individuals to manage their lives in a much more convenient way. In this paper, we propose a machine learning approach to classify the passenger’s tweets regarding the airplane services to understand the pattern of emotions. We adopt Random Forest (RF) and Logistic Regression (LR) to classify each tweet into positive, negative and neutral sentiment. The evaluation of the collected real data demonstrates that these two methods are able to achieve an accuracy ≈80%.