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.
KEYWORDS
social media, social networking, natural language processing, social computing, big data, semantic analysis, artificial intelligence, deep learning
To my husband Massoud, and my daughters, Tina and Amanda, who are just about the best children a mom could hope for: happy, loving, and fun to be with.
– Anna Atefeh Farzindar
To my wonderful husband Nicu with whom I can climb any mountain, and to our sweet daughter Nicoleta.
– Diana Inkpen
Contents
1 Introduction to Social Media Analysis
1.2.1 Cross-language Document Analysis in Social Media Data
1.2.2 Deep Learning techniques for Social Media Data
1.3 Challenges in Social Media Data
1.4 Semantic Analysis of Social Media
2 Linguistic Pre-processing of Social Media Texts
2.2 Generic Adaptation Techniques for NLP Tools
2.2.2 Re-training NLP Tools for Social Media Texts
2.7 Existing NLP Toolkits for English and Their Adaptation
2.8 Multi-linguality and Adaptation to Social Media Texts
3 Semantic Analysis of Social Media Texts
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
