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Overview: 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 book, comprising thirty contributions, is divided into four parts: (1). Statistical methods for Textual Data Analysis, (2). Advances in language processing, (3). Emotion and Sentiment Analyses, and (4). Textual Data Analysis in action. Machine learning has become a crucial technology in various industries and has many applications ranging from computer vision and natural language processing to predictive policing and healthcare. Artificial Intelligence (AI) models can be grouped into several families based on their underlying architecture, functionality, and applications. The most important ones are the following: 1. Deep Learning models: They have been the driving force behind many breakthroughs in AI in the recent year. Their architecture, made of multiple neuronal layers, is designed to model complex relationships and nonlinear interactions between input and output variables. 2. Generative models: They can generate new data much of a muchness to the fed training data. They are used in most of applications generating new images, music, or text.
Genre: Non-Fiction > Tech & Devices
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