Springer Text Analytics: Advances and Challenges (Studies in Classification, Data Analysis, and Knowledge Organization)

Focusing on methodologies, applications and challenges of textual data analysis and related fields, this book gathers selected and peer-reviewed contributions presented at the 14th International Conference on Statistical Analysis of Textual Data (JADT 2018), held in Rome, Italy, on June 12-15, 2018. Statistical analysis of textual data is a multidisciplinary field of research that has been mainly fostered by statistics, linguistics, mathematics and computer science. The respective sections of the book focus on techniques, methods and models for text analytics, dictionaries and specific languages, multilingual text analysis, and the applications of text analytics. The interdisciplinary contributions cover topics including text mining, text analytics, network text analysis, information extraction, sentiment analysis, web mining, social media analysis, corpus and quantitative linguistics, statistical and computational methods, and textual data in sociology, psychology, politics, law and marketing. Review “Readership: Graduate and advanced undergraduate statistics students, as well as practitioners. … Text Analytics: Advances and Challenges is an interesting read. … For students of text analysis and practitioners who are interested in applying text analysis methods to real problems, this text will be of interest.” (Jordan Rodu, International Statistical Review, June 2, 2021) From the Back Cover Focusing on methodologies, applications and challenges of textual data analysis and related fields, this book gathers selected and peer-reviewed contributions presented at the 14th International Conference on Statistical Analysis of Textual Data (JADT 2018), held in Rome, Italy, on June 12-15, 2018. Statistical analysis of textual data is a multidisciplinary field of research that has been mainly fostered by statistics, linguistics, mathematics and computer science. The respective sections of the book focus on techniques, methods and models for text analytics, dictionaries and specific languages, multilingual text analysis, and the applications of text analytics. The interdisciplinary contributions cover topics including text mining, text analytics, network text analysis, information extraction, sentiment analysis, web mining, social media analysis, corpus and quantitative linguistics, statistical and computational methods, and textual data in sociology, psychology, politics, law and marketing. About the Author Domenica Fioredistella Iezzi is an Associate Professor of Social Statistics at the Department of Enterprise Engineering Mario Lucertini, Tor Vergata University of Rome, Italy. She teaches courses on exploratory methods for data analysis and social media analytics. She is qualified as a Full Professor of Demography and Social Statistics and has been the director of the Master’s program in Data Science since 2014. A past advisor to the Italian Society of Demography and Statistics and the Italian Statistical Society, she has authored numerous scientific articles in national and international journals. Her main research topics include text clustering and social indicators. Damon Mayaffre is a CNRS researcher and a Professor at the Nice Côte d’Azur University, France. He is a specialist in the statistical analysis of textual data and has published several books on the political discourse of French presidents. Michelangelo Misuraca is an Associate Professor of Statistics for Social Sciences at the Department of Business Administration and Law, University of Calabria, Italy. He has taught courses on textual statistics and statistics for the social sciences at the University of Naples Federico II and the University of Calabria. A Fellow of the Italian Statistical Society and of the Royal Statistical Society, his research interests are mainly in the areas of textual statistics, text mining and social media mining.