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020 _a9783031021671
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024 7 _a10.1007/978-3-031-02167-1
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
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072 7 _aCOM004000
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100 1 _aFarzindar, Atefeh.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983858
245 1 0 _aNatural Language Processing for Social Media, Second Edition
_h[electronic resource] /
_cby Atefeh Farzindar, Diana Inkpen.
250 _a2nd ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIX, 175 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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_2rda
490 1 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
505 0 _aPreface to the Second Edition -- Acknowledgments -- Introduction to Social Media Analysis -- Linguistic Pre-processing of Social Media Texts -- Semantic Analysis of Social Media Texts -- Applications of Social Media Text Analysis -- Data Collection, Annotation, and Evaluation -- Conclusion and Perspectives -- Glossary -- Bibliography -- Authors' Biographies -- Index.
520 _aIn 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 which extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. We 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 (big data), and shows how innovative NLP approaches can integrate appropriate linguistic information in various fields such as social media monitoring, healthcare, business intelligence, industry, marketing, and security and defence. We review 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) or by the National Institute of Standards and Technology via the Text REtrieval Conference (TREC) and the Text Analysis Conference (TAC). In the concluding chapter, we discuss the importance of this dynamic discipline and its great potential for NLP in the coming decade, in the context of changes in mobile technology, cloud computing, virtual reality, and social networking. In this second edition, we have added information about recent progress in the tasks and applications presented in the first edition. We discuss new methods and their results. The number of research projects and publications that use social media data is constantly increasing due to continuously growing amounts of social media data and the need to automatically process them. We have added 85 new references to the more than 300 references from the first edition. Besides updating each section, we have added a new application (digital marketing) to the section on media monitoring and we have augmented the section on healthcare applications with an extended discussion of recent research on detecting signs of mental illness from social media.
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
700 1 _aInkpen, Diana.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983860
710 2 _aSpringerLink (Online service)
_983863
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031001789
776 0 8 _iPrinted edition:
_z9783031010392
776 0 8 _iPrinted edition:
_z9783031032950
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_983864
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02167-1
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