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Natural language processing for social media /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	006.35
書名/作者:	Natural language processing for social media // Atefeh Farzindar, Diana Inkpen.
作者:	Farzindar, Atefeh,
其他作者:	Inkpen, Diana,
面頁冊數:	1 PDF (xix, 175 pages) : : illustrations.
附註:	Part of: Synthesis digital library of engineering and computer science.
標題:	Social media.
標題:	Natural language processing (Computer science)
ISBN:	9781681736136
書目註:	Includes bibliographical references (pages 133-172) and index.
內容註:	1. Introduction to social media analysis -- 1.1 Introduction -- 1.2 Social media applications -- 1.2.1 Cross-language document analysis in social media data -- 1.2.2 Real-world applications -- 1.3 Challenges in social media data -- 1.4 Semantic analysis of social media -- 1.5 Summary --
摘要、提要註:	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 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.
電子資源:	http://ieeexplore.ieee.org/servlet/opac?bknumber=8239762

Natural language processing for social media /
Farzindar, Atefeh,

Natural language processing for social media /Atefeh Farzindar, Diana Inkpen. - Second edition. - 1 PDF (xix, 175 pages) :illustrations. - Synthesis lectures on human language technologies,# 381947-4059 ;. - Synthesis lectures on human language technologies ;30.

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 133-172) and index.

1. Introduction to social media analysis -- 1.1 Introduction -- 1.2 Social media applications -- 1.2.1 Cross-language document analysis in social media data -- 1.2.2 Real-world applications -- 1.3 Challenges in social media data -- 1.4 Semantic analysis of social media -- 1.5 Summary --

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Google book search

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 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.

System requirements: Adobe Acrobat Reader.

ISBN: 9781681736136

Standard No.: 10.2200/S00809ED2V01Y201710HLT038doiSubjects--Topical Terms:

350802
Social media.
Subjects--Index Terms:

social media

LC Class. No.: QA76.9.N38 / F275 2018

Dewey Class. No.: 006.35

Natural language processing for social media /
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245 1 0 $a Natural language processing for social media / $c Atefeh Farzindar, Diana Inkpen.
250 $a Second edition.
264 1 $a San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : $b Morgan & Claypool, $c 2018.
300 $a 1 PDF (xix, 175 pages) : $b illustrations.
336 $a text $2 rdacontent
337 $a electronic $2 isbdmedia
338 $a online resource $2 rdacarrier
490 1 $a Synthesis lectures on human language technologies, $x 1947-4059 ; $v # 38
500 $a Part of: Synthesis digital library of engineering and computer science.
504 $a Includes bibliographical references (pages 133-172) and index.
505 0 $a 1. Introduction to social media analysis -- 1.1 Introduction -- 1.2 Social media applications -- 1.2.1 Cross-language document analysis in social media data -- 1.2.2 Real-world applications -- 1.3 Challenges in social media data -- 1.4 Semantic analysis of social media -- 1.5 Summary --
505 8 $a 2. Linguistic pre-processing of social media texts -- 2.1 Introduction -- 2.2 Generic adaptation techniques for NLP tools -- 2.2.1 Text normalization -- 2.2.2 Re-training NLP tools for social media texts -- 2.3 Tokenizers -- 2.4 Part-of-speech taggers -- 2.5 Chunkers and parsers -- 2.6 Named entity recognizers -- 2.7 Existing NLP toolkits for English and their adaptation -- 2.8 Multi-linguality and adaptation to social media texts -- 2.8.1 Language identification -- 2.8.2 Dialect identification -- 2.9 Summary --
505 8 $a 3. Semantic analysis of social media texts -- 3.1 Introduction -- 3.2 Geo-location detection -- 3.2.1 Mapping social media information on maps -- 3.2.2 Readily available geo-location information -- 3.2.3 Geo-location based on network infrastructure -- 3.2.4 Geo-location based on the social network structure -- 3.2.5 Content-based location detection -- 3.2.6 Evaluation measures for geo-location detection -- 3.3 Entity linking and disambiguation -- 3.3.1 Detecting entities and linked data -- 3.3.2 Evaluation measures for entity linking -- 3.4 Opinion mining and emotion analysis -- 3.4.1 Sentiment analysis -- 3.4.2 Emotion analysis -- 3.4.3 Sarcasm detection -- 3.4.4 Evaluation measures for opinion and emotion classification -- 3.5 Event and topic detection -- 3.5.1 Specified vs. unspecified event detection -- 3.5.2 New vs. retrospective events -- 3.5.3 Emergency situation awareness -- 3.5.4 Evaluation measures for event detection -- 3.6 Automatic summarization -- 3.6.1 Update summarization -- 3.6.2 Network activity summarization -- 3.6.3 Event summarization -- 3.6.4 Opinion summarization -- 3.6.5 Evaluation measures for summarization -- 3.7 Machine translation -- 3.7.1 Adapting phrase-based machine translation to normalize medical terms -- 3.7.2 Translating government agencies' tweet feeds -- 3.7.3 Hashtag occurrence, layout, and translation -- 3.7.4 Machine translation for Arabic social media -- 3.7.5 Evaluation measures for machine translation -- 3.8 Summary --
505 8 $a 4. Applications of social media text analysis -- 4.1 Introduction -- 4.2 Health care applications -- 4.3 Financial applications -- 4.4 Predicting voting intentions -- 4.5 Media monitoring -- 4.6 Security and defense applications -- 4.7 Disaster response applications -- 4.8 NLP-based user modeling -- 4.9 Applications for entertainment -- 4.10 NLP-based information visualization for social media -- 4.11 Government communication -- 4.12 Summary --
505 8 $a 5. Data collection, annotation, and evaluation -- 5.1 Introduction -- 5.2 Discussion on data collection and annotation -- 5.3 Spam and noise detection -- 5.4 Privacy and democracy in social media -- 5.5 Evaluation benchmarks -- 5.6 Summary --
505 8 $a 6. Conclusion and perspectives -- 6.1 Conclusion -- 6.2 Perspectives --
505 8 $a A. TRANSLI: a case study for social media analytics and monitoring -- A1. TRANSLI architecture -- A2. User interface -- Glossary -- Bibliography -- Authors' biographies -- Index.
506 $a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 $a Google book search
510 0 $a Google scholar
510 0 $a INSPEC
510 0 $a Compendex
520 3 $a 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 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.
530 $a Also available in print.
538 $a System requirements: Adobe Acrobat Reader.
538 $a Mode of access: World Wide Web.
588 $a Title from PDF title page (viewed on December 31, 2017).
650 0 $a Social media. $3 350802
650 0 $a Natural language processing (Computer science) $3 411876
653 $a social media
653 $a social networking
653 $a natural language processing
653 $a social computing
653 $a big data
653 $a semantic analysis
700 1 $a Inkpen, Diana, $e author. $3 729047
776 0 8 $i Print version: $z 9781681736143 $z 9781681736129
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830 0 $a Synthesis digital library of engineering and computer science. $3 461208
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