Three Essays on Opinion Mining of Social Media Texts

This dissertation research is a collection of three essays on opinion mining of social media texts. I explore different theoretical and methodological perspectives in this inquiry. The first essay focuses on improving lexicon-based sentiment classification. I propose a method to automatically generate a sentiment lexicon that incorporates knowledge from both the language domain and the content domain. This method learns word associations from a large unannotated corpus. These associations are used to identify new sentiment words. Using a Twitter data set containing 743,069 tweets related to the stock market, I show that the sentiment lexicons generated using the proposed method significantly outperforms existing sentiment lexicons in sentiment classification. As sentiment analysis is being applied to different types of documents to solve different problems, the proposed method provides a useful tool to improve sentiment classification. The second essay focuses on improving supervised sentiment classification. In previous work on sentiment classification, a document was typically represented as a collection of single words. This method of feature representation suffers from severe ambiguity, especially in classifying short texts, such as microblog messages. I propose the use of dependency features in sentiment classification. A dependency describes the relationship between a pair of words even when they are distant. I compare the sentiment classification performance of dependency features with a few commonly used features in different experiment settings. The results show that dependency features significantly outperform existing feature representations. In the third essay, I examine the relationship between social media sentiment and stock returns. This is the first study to test the bidirectional effects in this relationship. Based on theories in behavioral finance research, I speculate that social media sentiment does not predict stock return, but rather that stock return predicts social media sentiment. I empirically test a set of research hypotheses by applying the vector autoregression (VAR) model on a social media data set, which is much larger than those used in previous studies. The hypotheses are supported by the results. The findings have significant implications for both theory and practice

A Framework for Profiling Prospective Students in Higher Education

Prospective student acquisition is a prominent issue in higher education marketing. Noel-Levitz (2012) estimated that higher education institutions are losing as high as 75% of the prospects after receiving an inquiry. Another study reported that 80% of the students who decide to apply to a program were influenced by the post-inquiry communications they had received from the higher education institutions (Aarinen, 2012). This chapter attempts to study the underlying concepts from literature and design a framework to extract prospective student profiles and further extend a discussion on how these profiles can be used to address the prospect engagement

The Role of Espoused National Cultural Values in Cross-National Cultural IS Studies

Hofstede’s work on national culture has been extensively used in cross-national studies in the information systems discipline. In particular, many cross-national cultural researchers have used Hofstede’s cultural index. This study argues that espoused national cultural values should be measured when the unit of analysis of the cross-national cultural study is the individual. This study reviews cross-national studies published in eight IS journals and examines both cross-national studies and cross-national cultural studies. After that, this work provides rationales of why espoused national cultural values should be measured. Finally, we conclude that espoused national culture is more appropriate for individual behavior research

How to Achieve Goals in Digital Games: An Empirical Test of a Goal-Oriented Model in Pokemon GO

To effectively design digital games and gamified systems, it is important to properly understand the psychological and behavioral processes that players use to reach goals. Although numerous prior studies have examined individual adoption, use, and continued use of digital games, few attempts have been made to understand how people desire and strive to achieve goals. The objective of this study is to develop and test a model of individual achievement of goals in digital gaming. Drawing upon theories of goal-directed behavior, we propose a conceptual model describing goal setting, goal striving, goal attainment, and feedback evaluations in the context of mobile gaming. To empirically test the proposed model, we collected two sets of (cross-sectional and longitudinal) data from 407 users of Pokemon GO. The results generally indicate that goal-directed effort plays an important role in translating goal desire into goal attainment. In addition, we found prior game points and goal desire have interaction effects on goal-directed effort and the subsequent acquisition of game points. Finally, this study shows that action strategies such as in-game payment and deliberate planning have differential effects on goal-directed effort and satisfying experiences. Overall, our findings provide empirical support for the efficacy of our goal-oriented model as a theoretical tool for explaining the process of goal striving to obtain game points. Our findings not only have important implications for digital gaming but also contribute to emerging research on gamified systems

Higher superconducting transition temperature by breaking the universal pressure relation

By investigating the bulk superconducting state via dc magnetization measurements, we have discovered a common resurgence of the superconductive transition temperatures (Tcs) of the monolayer Bi2Sr2CuO6+{\delta} (Bi2201) and bilayer Bi2Sr2CaCu2O8+{\delta} (Bi2212) to beyond the maximum Tcs (Tc-maxs) predicted by the universal relation between Tc and doping (p) or pressure (P) at higher pressures. The Tc of under-doped Bi2201 initially increases from 9.6 K at ambient to a peak at ~ 23 K at ~ 26 GPa and then drops as expected from the universal Tc-P relation. However, at pressures above ~ 40 GPa, Tc rises rapidly without any sign of saturation up to ~ 30 K at ~ 51 GPa. Similarly, the Tc for the slightly overdoped Bi2212 increases after passing a broad valley between 20-36 GPa and reaches ~ 90 K without any sign of saturation at ~ 56 GPa. We have therefore attributed this Tc-resurgence to a possible pressure-induced electronic transition in the cuprate compounds due to a charge transfer between the Cu 3d_(x^2-y^2 ) and the O 2p bands projected from a hybrid bonding state, leading to an increase of the density of states at the Fermi level, in agreement with our density functional theory calculations. Similar Tc-P behavior has also been reported in the trilayer Br2Sr2Ca2Cu3O10+{\delta} (Bi2223). These observations suggest that higher Tcs than those previously reported for the layered cuprate high temperature superconductors can be achieved by breaking away from the universal Tc-P relation through the application of higher pressures.Comment: 13 pages, including 5 figure

Classifying facts and opinions in Twitter messages: a deep learning-basedapproach

Massive social media data present businesses with an immense opportunity to extract useful insights. However, social media messages typically consist of both facts and opinions, posing a challenge to analytics applications that focus more on either facts and opinions. Distinguishing facts and opinions may significantly improve subsequent analytics tasks. In this study, we propose a deep learning-based algorithm that automatically separates facts from opinions in Twitter messages. The algorithm outperformed multiple popular baselines in an experiment we conducted. We further applied the proposed algorithm to track customer complaints and found that it indeed benefits subsequent analytics applications

Socio Cognitive and Affective Processing in the Context of Team-Based Gamified ERP Training: Reflective and Impulsive Model

Team-based enterprise gamification is designed to support teamwork and increase productivity within the organization in order to derive positive business outcomes through its own employees. While there have been a number of studies on gamification, they have mainly focused in the individual as the unit of analysis. Based on Reflective and Impulsive Model, the purpose of this study is to examine the effects of team-level reflective and impulsive determinants in the context of gamified ERP training. Our research model proposes that team absorptive capacity and team mood influence individuals’ ERP competence and hedonic motivation, which in turn affect intention to learn about ERP systems

A Multi-Appeal Model of Persuasion for Online Petition Success: A Linguistic Cue-Based Approach

Online petitions have become a powerful tool used by the public to affect change in society. Despite the increasing popularity of these petitions, it remains unclear how the public consumes and interprets their content and how this helps the creators of online petitions achieve their goals. This study investigates how linguistic factors present in online petition texts influence petition success. Specifically, drawing upon the dual-process theory of persuasion and the moral persuasion literature, this study examines cognitive, emotional, and moral linguistic factors in petition texts and identifies how they contribute to the success or failure of online petitions. The results, which are based on an analysis of 45,377 petitions from Change.org, show that petitions containing positive emotions and enlightening information are more likely to succeed. Contrary to popular belief, petitions containing heavy cognitive reasoning and those emphasizing moral judgment are less likely to succeed. This study also exemplifies the use of an analytical approach for examining crowd-sourced content involving online political phenomena related to policy-making, governance, political campaigns, and large social causes