This volume presents a selection of articles which explore methodological and applicative aspects of textual data analysis. Divided into four parts, it begins by focusing on statistical methods, and then moves on to problems in quantitative language processing. After discussing the challenging task of text mining in relation to emotional and sentiment analyses, the book concludes with a collection of studies in the social sciences and public health which apply textual data analysis methods.
The refereed contributions were originally presented at the 16th International Conference on Statistical Analysis of Textual Data (JADT 2022), which took place in Naples, Italy, on July 6-8, 2022. The biennial JADT meeting discusses theories, problems, and practical uses of textual data analysis in various fields, sharing a quantitative approach to the study of lexical, textual, pragmatic or discursive features of information expressed in natural language.
Part I Statistical methods for Textual Data Analysis.- Statistical and
deep learning methods for a linguistic and literary analysis.- Statistical
profiling of Hybrid CNN-SVM effectiveness.- EMOtivo: a classifier for emotion
detection of Italian texts trained on a self-labelled corpus.- Community
detection and semantic analysis on Twitter. The case of No greenpass and No
vax movement in Italy.- Evaluating customer satisfaction through Amazon
reviews analysis: the Bluetooth earphones example.- Symmetric Non-Negative
Matrix Factorization for analysing the scientific production on day surgery.-
Social Media Effects on Sales: consumer sentiment in a state-space model.-
Part II Advances in language processing.- Quality enhancements in
experimental statistics: the Italian Social Mood on Economy Index.- A
Strategy to Identify the Peculiarity of a Lexicon in the Analysis of a
Corpus.- Neologisms and estrangement in a corpus of science-fiction.-
Automatic retrieving ofderived Quechua verbs.- A Stylometric profile of
Carmen Mola in the gender of her authorial persona and the contribution of
each author behind the pseudonym.- Automatic Genre Classification of Czech
Texts Based on Syntactic Functions.- Deep learning as an aid to text mining
in the choice of texts to lemmatise for a comparison corpus: a stylistic
study of Peter Damians letters.- Dialogic Process Analysis in Natural
Language Processing: an attempt to describe sense of reality and meaning of
textdata.- Multi-channel Convolutional Transformer and intertextuality: a
Latin case study.- Part III Emotional and Sentiment Analyses.- Opinion Mining
Hybrid Approach. An application to investigate the users political positions
in disinformative echo chambers.- Prediction of Italians sentiment during
the first COVID-19 lockdown through a weighted random forest balanced with
SMOTE algorithm.- Sentiment analysis on social network data: the Regional
Index RETI.- Integrating text mining and hermeneutic analysis: the case of
international volunteering biographies.- Emotional text mining and
multilingual corpora: The analysis of #Covid-19 on Twitter.- Emotional
Markers as Indicators of Investor Attitudes: EDA Sub - Process Proposal.-
The Spanish Model: an analysis of Spanish culture in Organ Donation through
the Emotional Text Mining.- Part IV Textual Data Analysis in action.-
Comparative analysis of national reports: the case of the Erasmus+ ECOLHE
Project.- Action Research in Psychology: the case of adoptive family.-
Uncovering Uncertainty in Narrative Economics: A Semantic Search Approach.-
Italian Institutional communication in pandemic period: a chronological
analysis of Prime Minister speeches.- What about corruption? A text analytics
method for a scoping literature review.- The stability of the discursive
framework of the Ministry of Foreign Affairs tested by textometry.-
Statistical analysis of textual data for longitudinal analysis. A studyon
postgraduate course participants' reflections.- Third Mission & VQR
2015-2019: A Bigrams Story.
Giuseppe Giordano is an Associate Professor of Statistics at the Department of Political and Social Studies, University of Salerno, Italy. He teaches Statistics and Analytics for the Social Sciences. His research interests are mainly in Multidimensional Data Analysis, Social Network Analysis, and Analysis of Complex Data Structures with a focus on real problems emerging in several applicative fields.Michelangelo Misuraca is an Associate Professor of Statistics for Social Sciences at the Department of Business Administration and Law, University of Calabria, Italy. He teaches Statistics for Social Sciences and Textual Statistics at the University of Calabria and at the University of Naples Federico II. He is a fellow of the Italian Statistical Society (SIS) and of the Royal Statistical Society (RSS). His primary research interests are in the domain of Text Mining and Social Media Mining
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