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In recent years, online social networking has revolutionized
interpersonal communication. The newer research on language
analysis in social media has been increasingly focusing on the
latter's impact on our daily lives, both on a personal and a
professional level. Natural language processing (NLP) is one of the
most promising avenues for social media data processing. It is a
scientific challenge to develop powerful methods and algorithms
that extract relevant information from a large volume of data
coming from multiple sources and languages in various formats or in
free form. This book will discuss the challenges in analyzing
social media texts in contrast with traditional documents. Research
methods in information extraction, automatic categorization and
clustering, automatic summarization and indexing, and statistical
machine translation need to be adapted to a new kind of data. This
book reviews the current research on NLP tools and methods for
processing the non-traditional information from social media data
that is available in large amounts, and it shows how innovative NLP
approaches can integrate appropriate linguistic information in
various fields such as social media monitoring, health care, and
business intelligence. The book further covers the existing
evaluation metrics for NLP and social media applications and the
new efforts in evaluation campaigns or shared tasks on new datasets
collected from social media. Such tasks are organized by the
Association for Computational Linguistics (such as SemEval tasks),
the National Institute of Standards and Technology via the Text
REtrieval Conference (TREC) and the Text Analysis Conference (TAC),
or the Conference and Labs of the Evaluation Forum (CLEF). In this
third edition of the book, the authors added information about
recent progress in NLP for social media applications, including
more about the modern techniques provided by deep neural networks
(DNNs) for modeling language and analyzing social media data.
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