Date of Award

Spring 2012

Degree Name

Bachelor of Science

Major

Computer Science

First Advisor

Madalene Spezialetti

Second Advisor

Ralph A. Morelli

Abstract

In the past few years, Twitter has become a major social networking service with over 200 million tweets made every day. With this newfound source of expanding information, can people stay up to date with what others are posting? Along with the increasing processing power of computers, is there a way computing can analyze tweets on a large scale? Moreover, can computers understand what people think based on what they post? This senior project explores this question by determining the positive or negative sentiment of twitter posts by using a machine learning algorithm called Support Vector Machines. Based on a labeled dataset of tweets, a parser then extracts present features in the text to create a vector. Once a collection of vectors is compiled, data is trained and tested to create a working model, which can then be evaluated to determine the effectiveness of the classifier. Based on a dataset of 359 tweets and 329 features, a model can accurately classify tweets as high as 74.84% using a linear classifier.

Comments

Senior thesis completed at Trinity College for the degree of Bachelor of Science in Computer Science.

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