| 000 | 05550nam a2200229Ia 4500 | ||
|---|---|---|---|
| 003 | NULRC | ||
| 005 | 20250520102820.0 | ||
| 008 | 250520s9999 xx 000 0 und d | ||
| 020 | _a9781627053884 | ||
| 040 | _cNULRC | ||
| 050 | _aQA 76.9 .F37 2015 | ||
| 100 |
_aFarzindar, Atefeh _eauthor |
||
| 245 | 0 |
_aNatural langauge processing for social media / _cAtefeh Farzindar and Diana Inkpen. |
|
| 260 |
_aSan Rafael, California : _bMorgan & Claypool Publishers, _cc2015 |
||
| 300 |
_axix, 146 pages : _billustrations ; _c24 cm. |
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| 504 | _aIncludes bibliographical references. | ||
| 505 | _a1. 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 -- 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 -- 3. Semantic analysis of social media texts -- 3.1 Introduction -- 3.2 Geo-location detection -- 3.2.1 Readily available geo-location information -- 3.2.2 Geo-location based on network infrastructure -- 3.2.3 Geo-location based on the social network structure -- 3.2.4 Content-based location detection -- 3.2.5 Evaluation measures for geo-location detection -- 3.3 Entity linking and disambiguation -- 3.3.1 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 versus unspecified event detection -- 3.5.2 New versus 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 Translating government agencies' tweet feeds -- 3.7.2 Hashtag occurrence, layout, and translation -- 3.7.3 Machine translation for Arabic social media -- 3.7.4 Evaluation measures for machine translation -- 3.8 Summary -- 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 Summary -- 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 -- 6. Conclusion and perspectives -- 6.1 Conclusion -- 6.2 Perspectives. | ||
| 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 | _aNATURAL LANGUAGE PROCESSING (COMPUTER SCIENCE) | ||
| 700 |
_aInkpen, Diana _eco-author |
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| 942 |
_2lcc _cBK |
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| 999 |
_c16008 _d16008 |
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