Browsing by Subject "Text mining"

Now showing 1 - 4 of 4

Consumer perception of brand in social media : 3Es as drivers of brand admiration
(2019-07-26) Kang, Jisoo; Wilcox, Gary B.
In postmodern market, consumers are increasingly exposed to a flood of brands in their everyday purchasing experiences. In an effort to help companies garner a strong and positive brand relationship with consumers, this paper presents an in-depth case study which implemented topic modeling to analyze unstructured contents on Twitter of three energy drink brands: Red Bull, Monster Energy, and Rockstar Energy. The twitter conversation on each brand was collected, preprocessed, and then analyzed in order to infer public perception for the brands. The result provides the insight to increase intangible brand assets such as brand loyalty and brand admiration by examining how much value customers perceive from the current offering of the brand in respect with the benefits of 3Es(enable, entice, enrich). Furthermore, the study reveals the value of topic modeling as a powerful technology to bring out business value from a massive amount of accessible social media data.
Mapping Cold War Cuba-Germanies Conceptual Frameworks through Castro Speech Data Base (1975-1990)
(2023-05-30) Rodríguez Alfonso, Adriana
For her fellowship, Adriana Rodríguez Alfonso created a dataset on Cuban-Germanies intellectual relations during the Cold War from the Fidel Castro’s speeches collections to visualize the main themes underscored as well as the semantic networks those ideas mobilized, in order to unveil the political and cultural imaginaries about both Germanies in the Caribbean island.
Mining of identity theft stories to model and assess identity threat behaviors
(2014-05) Yang, Yongpeng; Barber, Suzanne
Identity theft is an ever-present and ever-growing issue in our society. Identity theft, fraud and abuse are present and growing in every market sector. The data available to describe how these identity crimes are conducted and the consequences for victims is often recorded in stories and reports by the news press, fraud examiners and law enforcement. To translate and analyze these stories in this very unstructured format, this thesis first discusses the collection of identity theft data automatically using text mining techniques from the online news stories and reports on the topic of identity theft. The collected data are used to enrich the ITAP (Identity Threat Assessment and Prediction) Project repository under development at the Center for Identity at The University of Texas. Moreover, this thesis shows the statistics of common behaviors and resources used by identity thieves and fraudsters — identity attributes used to identify people, resources employed to conduct the identity crime, and patterns of identity criminal behavior. Analysis of these results should help researchers to better understand identity threat behaviors, offer people early warning signs and thwart future identity theft crimes.
NewsFerret : supporting identity risk identification and analysis through text mining of news stories
(2013-05) Golden, Ryan Christian; Barber, Suzanne
Individuals, organizations, and devices are now interconnected to an unprecedented degree. This has forced identity risk analysts to redefine what “identity” means in such a context, and to explore new techniques for analyzing an ever expanding threat context. Major hurdles to modeling in this field include the inherent lack of publicly available data due to privacy and safety concerns, as well as the unstructured nature of incident reports. To address this, this report develops a system for strengthening an identity risk model using the text mining of news stories. The system—called NewsFerret—collects and analyzes news stories on the topic of identity theft, establishes semantic relatedness measures between identity concept pairs, and supports analysis of those measures through reports, visualizations, and relevant news stories. Evaluating the resulting analytical models shows where the system is effective in assisting the risk analyst to expand and validate identity risk models.