Date of Award

5-2020

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Master of Electrical Engineering

Abstract

Social media are shaping users' attitudes and behaviors through spreading information anytime and anywhere. Monitoring user opinions on social media is an effective solution to measure users' preferences towards brands or events. Currently, supervised machine learning-based methods dominate this area. However, as far as we know, there is no comprehensive comparison of performances of different models to figure out which model will be better for individual datasets. The focus of this thesis is to compare the performance of different supervised machine learning models. In detail, we built six classifiers, including support vector machine, random forest, neural network, Adaboost, decision tree, and Naive Bayes on two datasets and compare their performance. Furthermore, we introduced feature selection to remove unrelated attributes to preprocess the data and compare performance by building classifiers on the preprocessed data. Experimental results show that without feature selection, there is no significant difference in the performance. After feature selection, random forest outperformed other classifiers.

Committee Chair/Advisor

Xishuang Dong

Committee Member

Lijun Qian

Committee Member

John Fuller

Committee Member

Xiangfang Li

Publisher

Prairie View A&M University

Rights

© 2021 Prairie View A & M University

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Date of Digitization

7-13-2021

Contributing Institution

John B Coleman Library

City of Publication

Prairie View

MIME Type

Application/PDF

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