Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications

Exploring Information Systems Curricula

“…As we consider unstructured data, for the next step we rely on the use of text mining methods to process the data. Integrating our task definition into the framework of Miner et al (2012) led us to the field of concept extraction. The text mining methods there are summarized under the term ''topic modeling''.…”

TextWiller: Collection of functions for text mining, specially devoted to the Italian language

“…The main differences compared to other popular packages like tm are that TextWiller allows sentiment analysis; it includes classification tools for Italian cities and names; and it can help social media researchers with some specific functions for the data extracted from a social networking site via APIs. In particular, TextWiller allows to normalize (Miner et al (2012), Bolasco & De Mauro (2013)) Italian text, i.e. transforming a corpus in a canonical form, useful for text mining.…”

Text Mining for Precision Medicine

“…Text mining helps in the acquisition and extraction of hidden information or trends that researchers fail to capture from large-scale and content-rich text data (Aggarwal & Zhai, 2012). Assigning a comprehensive definition to TM is hard since the field emerged out of multiple related but distinct disciplines including data mining techniques, information extraction, information retrieval, machine learning, natural language processing, computational linguistics, statistical data analysis, linear geometry, probability theory, and even Graph Theory (Miner, Elder, & Nisbet, 2012). It could also be referred to as text analytics, intelligent text analysis, text data mining, or knowledge Discovery in Text (KDT), but they all refer to the process of analyzing and processing semi-structured and unstructured text data.…”

“…It could also be referred to as text analytics, intelligent text analysis, text data mining, or knowledge Discovery in Text (KDT), but they all refer to the process of analyzing and processing semi-structured and unstructured text data. Figure 1.1 illustrates the major fields that overlap with text mining (Miner et al, 2012). This section introduces in-depth three of the related field relevant to the research conducted in this thesis: Natural Language Processing (NLP), Information Extraction (IE) and Machine Learning (ML).…”