Text Analytics

1. Front Matter

   Pages i-xi

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2. Techniques, Methods and Models

   1. Front Matter

      Pages 1-1

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   2. Text Analytics: Present, Past and Future

      • Domenica Fioredistella Iezzi, Livia Celardo

      Pages 3-15

   3. Unsupervised Analytic Strategies to Explore Large Document Collections

      • Michelangelo Misuraca, Maria Spano

      Pages 17-28

   4. Studying Narrative Flows by Text Analysis and Network Text Analysis

      • Cristiano Felaco, Anna Parola

      Pages 29-40

   5. Key Passages : From Statistics to Deep Learning

      • Laurent Vanni, Marco Corneli, Dominique Longrée, Damon Mayaffre, Frédéric Precioso

      Pages 41-53

   6. Concentration Indices for Dialogue Dominance Phenomena in TV Series: The Case of the Big Bang Theory

      • Andrea Fronzetti Colladon, Maurizio Naldi

      Pages 55-64

   7. A Conversation Analysis of Interactions in Personal Finance Forums

      • Maurizio Naldi

      Pages 65-74

3. Dictionaries and Specific Languages

   1. Front Matter

      Pages 75-75

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   2. Big Corpora and Text Clustering: The Italian Accounting Jurisdiction Case

      • Domenica Fioredistella Iezzi, Rosamaria Berté

      Pages 77-89

   3. Lexicometric Paradoxes of Frequency: Comparing VoBIS and NVdB

      • Luisa Revelli

      Pages 91-99

   4. Emotions and Dense Words in Emotional Text Analysis: An Invariant or a Contextual Relationship?

      • Nadia Battisti, Francesca Romana Dolcetti

      Pages 101-116

   5. Text Mining of Public Administration Documents: Preliminary Results on Judgments

      • Maria Francesca Romano, Antonella Baldassarini, Pasquale Pavone

      Pages 117-126

   6. Using the First Axis of a Correspondence Analysis as an Analytic Tool

      • Bénédicte Pincemin, Alexei Lavrentiev, Céline Guillot-Barbance

      Pages 127-143

   7. Discursive Functions of French Modal Forms: What Can Correspondence Analysis Tell Us About Genre and Diachronic Variation?

      • Corinne Rossari, Ljiljana Dolamic, Annalena Hütsch, Claudia Ricci, Dennis Wandel

      Pages 145-157

4. Multilingual Text Analysis

   1. Front Matter

      Pages 159-159

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   2. How to Think About Finding a Sign for a Multilingual and Multimodal French-Written/French Sign Language Platform?

      • Cédric Moreau

      Pages 161-166

   3. Corpus in “Natural” Language Versus “Translation” Language: LBC Corpora, A Tool for Bilingual Lexicographic Writing

      • Annick Farina, Riccardo Billero

      Pages 167-178

   4. The Conditional Perfect, A Quantitative Analysis in English-French Comparable-Parallel Corpora

      • Daniel Henkel

      Pages 179-197

   5. Repeated and Anaphoric Segments Applied to Trilingual Knowledge Extraction

      • Lionel Shen

      Pages 199-211

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. 

• textual data analysis
• text mining
• statistical analysis of textual data
• text analytics
• sentiment analysis
• information extraction
• opinion mining
• social media analysis
• lexical resources
• web mining
• corpus and quantitative linguistics
• chronological corpora
• network text analytics
• textual data in psychology
• text mining in the social sciences
• big data
• computational linguistics
• textual statistics

“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)

• Department of Enterprise Engineering Mario Lucertini, Tor Vergata University, Rome, Italy

  Domenica Fioredistella Iezzi

• BCL, University of Côte d’Azur and CNRS, Nice, France

  Damon Mayaffre

• Department of Business Administration and Law, University of Calabria, Rende, Italy

  Michelangelo Misuraca

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.

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