Efficient sentiment classification of Twitter feeds
Nicholas Chamansingh, Patrick A. Hosein · 2016
Sentiment Analysis encompasses the use of Natural Language Processing together with statistics and machine learning methods for the identification, extraction and characterization of sentiment elements from a body of text. Micro-blog platforms, such as Twitter, allows for the sharing of real-time comments and opinions from millions of users on various topics. This research presents an experiment to determine an efficient sentiment classifier of real-time Twitter feeds. Naive Bayes, Support Vector Machine (SVM) and Maximum Entropy (MaxEnt) classification methods were compared. For each approach we used the same pre-processing and feature selection methods. Chi-Square feature selection was used to determine the smallest feature set and training data size needed for a classifier with a given accuracy level, storage requirements and classification time. Results show that, when compared to previous work, a significant reduction in data input and processing can be achieve while maintaining an acceptable level of accuracy.