Date of Graduation

5-2019

Document Type

Thesis

Degree Name

Bachelor of Science

Degree Level

Undergraduate

Department

Computer Science and Computer Engineering

Advisor/Mentor

Gauch, Susan

Committee Member/Reader

Gauch, John

Committee Member/Second Reader

Thompson, Dale

Abstract

This paper presents research applying Emotional Analysis to “Fake News” and “Real News” articles to investigate whether or not there is a difference in the emotion used in these two types of news articles. The paper reports on a dataset for Fake and Real News that we created, and the natural language processing techniques employed to process the collected text. We use a lexicon that includes predefined words for eight emotions (anger, anticipation, disgust, fear, surprise, sadness, joy, trust) to measure the emotional impact in each of these eight dimensions. The results of the emotion analysis are used as features for machine learning algorithms contained in the Weka package to train a classifier. This classifier is then used to analyze a new document to predict/classify it to be “Fake” or “Real” News.

Keywords

emotion analysis, machine learning, classifier, fake news, service learning

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