Date of Award

2018

Publisher

North Dakota State University

Document Type

Master's Paper

Degree Awarded

Master of Science (MS)

Program

Software Engineering

Department

Computer Science

College

Engineering

Faculty Advisor

Ludwig, Simone

Description

Sentiment analysis is the process of determining opinion expressed in a text, or an estimation of emotion related to the certain topic if it is negative, positive or neutral. The massive growth of social media, Twitter has played an important role since it allows people to express their feelings about a subject. Classification algorithms are necessary in the process of sentiment analysis. In this paper, we build a model to acquire people’s opinion on any concerning subject and evaluate the classification algorithms on the dataset. To accomplish the goal, we use a large set of Tweets which refer to a particular topic and execute the analytics on the Twitter feeds to classify them by using Naïve Bayes, Support Vector Machine, Maximum Entropy and Boosting algorithms. Then, to obtain the result we measure the accuracy among the four algorithms and compare them to identify the best algorithm based on our experiment.

Rights

NDSU Policy 190.6.2

Rights Link

https://www.ndsu.edu/fileadmin/policy/190.pdf

Handle Identifier

https://hdl.handle.net/10365/29114

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