#Textual data analysis code#
Code and a variety of additional resources to enrich the use of this book are available on an accompanying website at. Textual analysis is the process of examining a text in order to understand its meaning. Currently, there is no introductory how-to book on textual data analysis with R that is up-to-date and applicable across the social sciences. It includes two major case studies using historical and more contemporary text data to demonstrate the practical applications of these methods. In this module, we focus on exploratory data analysis and practical data visualization, involving challenging, real-world datasets. Methods: The outline is applied to the corpus of twelve (12) episodes of Mann Ki Baat 2.0. The respective sections of the book focus on techniques, methods and models for text analytics, dictionaries and specific languages, multilingual text analysis, and the. Using techniques of textual data analytics, an analytical framework is designed in this research paper. Using up-to-date R methods, this book will take readers through the text analysis process, from text mining and pre-processing the text to final analysis. Statistical analysis of textual data is a multidisciplinary field of research that has been mainly fostered by statistics, linguistics, mathematics and computer science. Traditional texts on statistical analysis have focused on numbers, but this book will provide a practical introduction to the quantitative analysis of textual data. Distilling useful insights from a high-volume of text requires scalable analytical techniques employing AI, machine learning, and statistical methods, as well. Researchers in the social sciences and beyond are dealing more and more with massive quantities of text data requiring analysis, from historical letters to the constant stream of content in social media.
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