000 04196nam a22005175i 4500
001 978-3-031-02157-2
003 DE-He213
005 20240730163555.0
007 cr nn 008mamaa
008 221028s2015 sz | s |||| 0|eng d
020 _a9783031021572
_9978-3-031-02157-2
024 7 _a10.1007/978-3-031-02157-2
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aFarzindar, Atefeh.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979301
245 1 0 _aNatural Language Processing for Social Media
_h[electronic resource] /
_cby Atefeh Farzindar.
250 _a1st ed. 2015.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aIV, 166 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
505 0 _aPreface -- 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.
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 Natural Language Processing (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, health care, business intelligence, industry, marketing, and security and defense. 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, and social networking.
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
710 2 _aSpringerLink (Online service)
_979302
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031010293
776 0 8 _iPrinted edition:
_z9783031032851
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_979303
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02157-2
912 _aZDB-2-SXSC
942 _cEBK
999 _c84754
_d84754