Sentiment Analysis of Tweets Including Emoji Data
Document Type
Conference Proceeding
Publication Date
12-4-2018
Abstract
Sentiment analysis of text is a valuable tool used to identify and classify bodies of text for various purposes, including public sentiment detection in political campaigns, spam detection and threat assessment. In this paper, we examine the effectiveness of incorporating Emoji data for Twitter data emotion classification. We conducted experiments using Multinomial Naïve Bayes (MNB) and Support Vector Machine (SVM) classification methods, with automatically labeled Twitter data. We compare the accuracy of these classification methods with and without the Emoji data included over varying vocabulary sizes. We find that MNB outperforms SVM on the data for large vocabulary sizes, and both classifiers perform slightly better with the Emoji data included.
Publication Source (Journal or Book title)
Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017
First Page
793
Last Page
798
Recommended Citation
Lecompte, T., & Chen, J. (2018). Sentiment Analysis of Tweets Including Emoji Data. Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017, 793-798. https://doi.org/10.1109/CSCI.2017.137