Romine, Samuel_MCS_2022

Title Romine, Samuel_MCS_2022
Alternative Title Primary Texts and Their Effects on Sentiment and Emotional Analysis
Creator Romine, Samuel
Collection Name Master of Computer Science
Description This Masters of Computer Science thesis explores how sentiment sources do not have siginficant impact on prediction quality of machine learning models.
Abstract When performing sentiment analysis, it is common to derive sentiment from a multitude of sources, including lexicons, crowdsourcing, online tools, or even reading and analyzing the text yourself as the researcher. In my research I prove that you cannot simply derive your own sentiment from another person's text to test the validity of your sentiment analyzers, but that you must use the original author's sentiment as the basis for modeling the accuracy of various analyzers. To create such a set of data, I used MTurk, a surveying service provided by Amazon, to distribute various surveys to create primary annotated text. Using basic emotional theory presented by Paul Ekman, I was able to create data with annotated sentiment and emotion. In this paper I create a method to compare sentiment sources rather than sentiment analyzers, and I show that the source of sentiment does not have any statically significant impact on the prediction quality of various machine learning models. I also show that these models do not perform any better than the common person and are still able to be outclassed by subject matter experts. Finally, I present a brief exploration into emotional analysis and demonstrate that emotional analysis tools are still outclassed by humans performing the same task.
Subject Algorithms; Computational linguistics; Computer science; Machine learning
Keywords machine learning, emotinal intelligence, artificial intelligence, computer science
Digital Publisher Stewart Library, Weber State University, Ogden, Utah, United States of America
Date 2022
Medium Thesis
Type Text
Access Extent 70 page PDF; 1.83 MB
Language eng
Rights The author has granted Weber State University Archives a limited, non-exclusive, royalty-free license to reproduce their theses, in whole or in part, in electronic or paper form and to make it available to the general public at no charge. The author retains all other rights.
Source University Archives Electronic Records: Master of Computer Science. Stewart Library, Weber State University
OCR Text Show
Format application/pdf
ARK ark:/87278/s6v7t7js
Setname wsu_smt
ID 96891
Reference URL https://digital.weber.edu/ark:/87278/s6v7t7js