A Sentiment Analysis Using the Dialogic Loop
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Florida International University FIU Digital Commons FIU Electronic Theses and Dissertations University Graduate School 11-6-2019 Twitter Activity Of Urban And Rural Colleges: A Sentiment Analysis Using The Dialogic Loop Eugene H. Pons Florida International University, [email protected] Follow this and additional works at: https://digitalcommons.fiu.edu/etd Part of the Communication Technology and New Media Commons, Educational Technology Commons, Mass Communication Commons, and the Social Media Commons Recommended Citation Pons, Eugene H., "Twitter Activity Of Urban And Rural Colleges: A Sentiment Analysis Using The Dialogic Loop" (2019). FIU Electronic Theses and Dissertations. 4342. https://digitalcommons.fiu.edu/etd/4342 This work is brought to you for free and open access by the University Graduate School at FIU Digital Commons. It has been accepted for inclusion in FIU Electronic Theses and Dissertations by an authorized administrator of FIU Digital Commons. For more information, please contact [email protected]. FLORIDA INTERNATIONAL UNIVERSITY Miami, Florida TWITTER ACTIVITY OF URBAN AND RURAL COLLEGES: A SENTIMENT ANALYSIS USING THE DIALOGIC LOOP A dissertation submitted in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in CURRICULUM AND INSTRUCTION by Eugene H. Pons 2019 To: Dean Michael R. Heithaus College of Arts, Science and Education This dissertation, written by Eugene H. Pons and entitled Twitter Activity of Urban and Rural Colleges: A Sentiment Analysis Using the Dialogic Loop, having been approved in respect to style and intellectual content, is referred to you for judgement. We have read this dissertation and recommend that it be approved. _______________________________________ Leonardo Ferreira _______________________________________ Maria Lovett _______________________________________ Thomas Reio _______________________________________ M. O. Thirunarayanan, Major Professor Date of Defense: November 6, 2019 The dissertation of Eugene H. Pons is approved. _______________________________________ Dean Michael R. Heithaus College of Arts, Sciences and Education _______________________________________ Andrés G. Gil Vice President for Research and Economic Development and Dean of the University Graduate School Florida International University, 2019 ii © Copyright 2019 by Eugene H. Pons All rights reserved. iii DEDICATION This dissertation is dedicated to my mother Angela D. Pons and my aunt Angela M. Cruz, who encouraged me to advance my studies to the point of obtaining a doctoral degree. It is also dedicated to my brother, Alexander Pons, my wife, Jennifer Coccaro- Pons, and my children Sabrina and Nicholas, whose support and motivation pushed me to complete this journey. iv ACKNOWLEDGMENTS I would like to thank my brother, Alexander Pons, for his support through the difficult points of this challenging endeavor. Also my wife, Jennifer Coccaro-Pons, who convinced me to continue my studies and pursue a doctoral degree. They both provided emotional support throughout the entire process, sat through countless hours of discussion, provided extensive feedback, and helped me through the meticulous editing process. Without their invaluable backing and guidance, the completion of this endeavor would have been impossible. Also, my sincerest appreciation goes to Dr. M. O. Thirunarayanan for always providing valuable input and guidance, Dr. Leonardo Ferriera, Dr. Maria Lovett, and Dr. Thomas Reio for the time spent in reading this dissertation and providing valuable feedback. v ABSTRACT OF THE DISSERTATION TWITTER ACTIVITY OF URBAN AND RURAL COLLEGES: A SENTIMENT ANALYSIS USING THE DIALOGIC LOOP by Eugene H. Pons Florida International University, 2019 Miami, Florida Professor M. O. Thirunarayanan, Major Professor The purpose of the present study is to ascertain if colleges are achieving their ultimate communication goals of maintaining and attracting students through their microblogging activity, which according to Dialogic Loop Theory, is directly correlated to the use of positive and negative sentiment. The study focused on a cross-section of urban and rural community colleges within the United States to identify the sentiment score of their microblogging activity. The study included a content analysis on the Twitter activity of these colleges. A data-mining process was employed to collect a copious of the tweets associated with these colleges. Further processing was then applied using data linguistic software that removed all irrelevant text, word abbreviations, emoticons, and other Twitter specific classifiers. The resulting data set was then processed through a Multinomial Naive Bayes Classifier, which refers to a probability of word counts in a text. The classifier was trained using a data source of 1.5 million tweets, called Sentiment140 that analyzed the corpus of these tweets, labeling them as positive and negative sentiment. The Multinomial Naive Bayes Classifier distinguished specific wording and phrases from the corpus, comparing the data to a specific database of vi sentiment word identifiers. The sentiment analysis process categorized the text as being positive or negative. Finally, statistical analysis was conducted on the outcome of the sentiment analysis. A significant contribution of the current work was extending Kent and Taylor's (1998) Dialogic Loop Theory, which was designed specifically for identifying the relationship building capabilities of a Web site, to encompass the microblogging concept used in Twitter. Specifically, Dialogic Loop Theory is applied and enhanced to develop a model for social media communication to augment relationship building capabilities, which the current study established as a new form for evaluating Twitter tweets, labeled in the current body of work as Microblog Dialogic Communication. The implication is that by using Microblog Dialogic Communication, a college can address and correct their microblogging sentiment. The results of the data collected found that rural colleges tweeted more positive sentiment tweets and less negative sentiment tweets when compared to the urban colleges’ tweets. vii TABLE OF CONTENTS CHAPTER PAGE CHAPTER I: INTRODUCTION ........................................................................................ 2 Pilot Study – Tweeter Sentiment Analysis ...................................................................... 9 Build Classification Model to perform Sentiment Analysis ......................................... 11 Analyze the tweets from a college ................................................................................ 12 Purpose Statement ......................................................................................................... 14 Background ................................................................................................................... 16 Significance of the Study .............................................................................................. 20 Research Questions ....................................................................................................... 29 Hypotheses .................................................................................................................... 31 Assumptions .................................................................................................................. 32 Delimitations ................................................................................................................. 32 Summary ....................................................................................................................... 33 Definition of Terms ....................................................................................................... 34 CHAPTER II. Literature Review ...................................................................................... 37 Dialogic Loop Theory ................................................................................................... 38 Content Analysis ........................................................................................................... 47 Sentiment Analysis ........................................................................................................ 53 Machine Learning Classifiers ........................................................................................ 58 Naive Bayes ................................................................................................................... 59 Multinomial Naive Bayes .............................................................................................. 61 Maximum Entropy ........................................................................................................ 61 Support Vector Machines .............................................................................................. 62 Social Media and Microblogging .................................................................................. 64 Summary ....................................................................................................................... 70 CHAPTER III. METHODOLOGY .................................................................................. 73 Data Source ................................................................................................................... 75 Data Set - Twitter API - Twitter Data Collection ......................................................... 76 Register Data Extraction Application with Twitter -