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La obtención de información a partir de un conjunto de datos o minería de datos es una tarea compleja que involucra varias etapas, tal como sucede en la minería de texto. Esta puede ser considerada como un caso particular de minería de datos donde los datos contemplan la incorporación de texto. Ambos procesos de minería se vuelven aun más complejos cuando nos encontramos ante grandes cúmulos de datos o texto. Es común encontrar conjuntos de datos grandes, complejos y ricos en información en áreas como medicina, comercio, ingeniería y ciencias de la computación. Simultáneamente, los avances tecnológicos han dado lugar a la acumulación de sustanciosas cantidades de documentos, artículos y texto; el ejemplo más contundente de esta clase de material es la Web, la cual se estima que alcanza más de 8.05 billones de páginas. La propuesta de esta tesis es el uso de herramientas evolutivas mono- y multi-objetivo como un soporte para algunas de las etapas de este proceso. En particular, las etapas que implican optimización y búsqueda dentro de estos grandes espacios en los cuales otros métodos serían inviables. A lo largo de la investigación se desarrollaron, evaluaron y compararon algoritmos evolutivos mono y multi-objetivo tanto para la rama de minería de datos como para la rama de minería de texto. Como caso particular dentro de minería de datos, se contempló el problema de encontrar las relaciones más relevantes entre variables dentro de distintos conjuntos de datos. Dichas relaciones, no son visibles para un experto cuando se encuentra frente a la base de datos original cruda, la cual puede contemplar miles de variables y miles de instan-cias. Para resolver este problema se propuso una metodología de dos fases. Los algoritmos desarrollados en este contexto se integraron a la primera fase de la arquitectura y fueron exitosamente utilizados como mecanismo de búsqueda masiva. Por otra parte, en el caso de minería de texto se abordó el problema de recuperar información relacionada y novedosa con respecto a un tópico de interés. Para este problema se propuso, implementó y evaluó una arquitectura que, partiendo de una descripción para el tópico de interés, evoluciona varios conjuntos de términos hacia conjuntos que logren obtener mejores documentos con respecto a dicho tema de interés y con respecto a los objetivos propuestos (por ejemplo: simi-litud, precisión, cobertura). Dentro de las técnicas evolutivas multi-objetivo propuestas, se diseñaron adaptaciones de los algoritmos basados en Pareto más prometedores reportados por la literatura y se propusieron versiones multi-objetivo agregativas. Ambos enfoques, los basados en Pareto y los agregativos, demostraron ser claramente competentes tanto para minería de datos como para minería de texto. / Data mining comprises the capture of information from data, which is a complex task that involves many stages. The same applies to text mining that can be considered as a special case of data mining where the data include text. As data and text sets increase, both mining processes become even more complicated. Large, complex and rich information data sets arise in many common research elds like medicine, commerce, engineering and computer science. Simultaneously, techno-logical advances have led to theaccumulation of substantial amounts of documents, articles and text; the clearest example of this kind of material is the Web, which is estimated to have reached more than 8.05 billion pages. This thesis proposes the use of mono- and multi-objective evolutionary tools as support in some of the stages of the data and text mining processes. In particular, those stages which imply optimiza-tion and search in wide search spaces where other methods could be unfeasible. In this research work, several mono- and multi-objective evolutionary algorithms were developed, evaluated and compared for both, data and text mining research areas. As a particular case in data mining, the problem of finding the most relevant relationship among variables from the data was considered. These relations, are not obvious for experts when they are faced with the original raw database, which can include thousands of variables and thousand of samples. In order to solve this problem, a two-phase methodology was proposed. In this context, the developed algorithms were integrated into the first phase and were succesfully used as massive search mechanisms. On the other hand, as a particular case of the text mining research area, the problem of retrieving novel material that is related to a search context was considered. In order to overcome this problem, an architecture was proposed, implemented and evaluated. Starting from a description for the topic of interest, this architecture evolves several sets of terms towards sets which can obtain better documents with respect to both, the topic of interest and the proposed objectives (e.g., similarity, precision, recall). Among the proposed multi-objetive evolutionary techniques, adap-tations of the more promising reported Pareto-based evolutionary algorithms were designed and new multi-objective aggregative schemes were proposed. Both approaches- i.e., the Pareto-based strategy and the aggregative techniques- proved to be clearly competent for both research areas: data and text mining.

No / The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anti-counterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progress with contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis; the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.

This study applied both human-based and computer-based techniques to conduct a job analysis in the field of instructional technology. The primary research focus of the job analysis was to examine the efficacy of text mining by comparing text mining results with content analysis results. This agenda was fulfilled by using job announcement data as an example to determine essential job categories and required competencies. In phase one, a job title analysis was conducted. Different categorizing strategies were explored, and primary job categories were reported. In phase two, the human-based content analysis was conducted, which identified 20 competencies in the knowledge domain, 22 in the ability domain, 23 in the skill domain, and 13 other competencies. In phase three, text mining (topic modeling) was applied to the entire data set, resulting in 50 themes. From these 50 themes, the researcher selected 20 themes that were most relevant to instructional technology competencies. The findings of the two research techniques differ in terms of granularity, comprehensibility, and objectivity. Based on evidence revealed in the current study, the author recommends that future studies explore ways to combine the two techniques to complement one another. / Doctor of Philosophy / According to Kimmons and Veletsianos (2018), text mining has not been widely applied in the field of instructional technology. This study provides an example of using text mining techniques to discover a set of required job competencies. It can be helpful to researchers unfamiliar with text mining methodology, allowing them to understand its potentials and limitations better. The primary research focus was to examine the efficacy of text mining by comparing text mining results with content analysis results. Both content analysis and text mining procedures were applied to the same data set to extract job competencies. Similarities and differences between the results were compared, and the pros and cons of each methodology were discussed.

Yes / This study uncovers hotel brand positioning and competitive landscape mapping by text-mining user-generated content (UGC). Rather than relying on a single dimension of consumer evaluation, the current study detects brand attributes by using both customer preferences as well as perceptual performance to develop meaningful insights. For this, the study combines content analysis and repertory grid analysis (RGA) to answer three key research issues. 111,986 hotel reviews from two biggest Chinese cities are used to explore and visualize the competitive landscape of six selected hotel brands across three hotel categories. Findings from the study will not only advance the existing literature on brand positioning and competitive landscape mapping but also help practitioners in developing brand positioning strategies to fight competitors within and across hotel categories.

Yes / The rapid rise of many e-commerce platforms for individual consumers has generated a large amount of text-based data, and thus researchers have begun to experiment with text mining techniques to extract information from the large amount of textual data to assist in sales forecasting. The existing literature focuses textual data on product reviews; however, consumer reviews are not something that companies can directly control, here we argue that textual product descriptions are also important determinants of consumer choice. We construct an artificial intelligence (AI) framework that combines text mining, WaveNet neural networks, multiple regression, and SHAP model to explain the impact of product descriptions on sales forecasting. Using data from nearly 200,000 sales records obtained from a cross-border e-commerce firm, an empirical study showed that the product description presented to customers can influence sales forecasting, and about 44% of the key phrases greatly affect sales forecasting results, the sales forecasting models that added key product description phrases had improved forecasting accuracy. This paper provides explainable results of sales forecasting, which can provide guidance for firms to design product descriptions with reference to the market demand reflected by these phrases, and adding these phrases to product descriptions can help win more customers. / The full-text of this article will be released for public view at the end of the publisher embargo on 24 Feb 2025.

Die vorliegende Masterarbeit behandelt den Entwurf und die exemplarische Umsetzung eines Arbeitsablaufs zur Aufbereitung von georeferenziertem Microblogging Content. Als beispielhafte Datenquelle wurde Twitter herangezogen. Darauf basierend, wurden Überlegungen angestellt, welche Arbeitsschritte nötig und mit welchen Mitteln sie am besten realisiert werden können. Dabei zeigte sich, dass eine ganze Reihe von Bausteinen aus dem Bereich des Data Mining und des Text Mining für eine Pipeline bereits vorhanden sind und diese zum Teil nur noch mit den richtigen Einstellungen aneinandergereiht werden müssen. Zwar kann eine logische Reihenfolge definiert werden, aber weitere Anpassungen auf die Fragestellung und die verwendeten Daten können notwendig sein. Unterstützt wird dieser Prozess durch verschiedenen Visualisierungen mittels Histogrammen, Wortwolken und Kartendarstellungen. So kann neues Wissen entdeckt und nach und nach die Parametrisierung der Schritte gemäß den Prinzipien des Geovisual Analytics verfeinert werden. Für eine exemplarische Umsetzung wurde nach der Betrachtung verschiedener Softwareprodukte die für statistische Anwendungen optimierte Programmiersprache R ausgewählt. Abschließend wurden die Software mit Daten von Twitter und Flickr evaluiert. / This Master's Thesis deals with the conception and exemplary implementation of a workflow for georeferenced Microblogging Content. Data from Twitter is used as an example and as a starting point to think about how to build that workflow. In the field of Data Mining and Text Mining, there was found a whole range of useful software modules that already exist. Mostly, they only need to get lined up to a process pipeline using appropriate preferences. Although a logical order can be defined, further adjustments according to the research question and the data are required. The process is supported by different forms of visualizations such as histograms, tag clouds and maps. This way new knowledge can be discovered and the options for the preparation can be improved. This way of knowledge discovery is already known as Geovisual Analytics. After a review of multiple existing software tools, the programming language R is used to implement the workflow as this language is optimized for solving statistical problems. Finally, the workflow has been tested using data from Twitter and Flickr.