Download Free Infinite Word Topic Models For Digital Media Book in PDF and EPUB Free Download. You can read online Infinite Word Topic Models For Digital Media and write the review.

Describes recent academic and industrial applications of topic models with the goal of launching a young researcher capable of building their own applications of topic models.
The digital, in the form of technologies, scenarios, objects, processes, and relational and interactional structures, is increasingly becoming central to understanding culture, society, human experience, and the social world. It permeates our society’s practices, symbols, and shared meanings, and it makes old distinctions, such as the one between online and offline, real and virtual, and material and immaterial, obsolete. It also introduces digitally native objects of research, such as cyber-bullying and digital identities, which have a direct impact on mainstream sociological problems.
The 9 papers presented in this book are revised and significantly extended versions of papers submitted to three related workshops: The 5th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2014, and the First International Workshop on Machine Learning for Urban Sensor Data, SenseML 2014, which were held on September 15, 2014, in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2014) in Nancy, France; and the 5th International Workshop on Modeling Social Media (MSM 2014) that was held on April 8, 2014 in conjunction with ACM WWW in Seoul, Korea.
This book constitutes the thoroughly refereed papers of the 4th National Conference of Social Media Processing, SMP 2015, held in Guangzhou, China, in November 2015. The 14 revised full papers and 9 short papers presented were carefully reviewed and selected from 105 submissions. The papers address issues such as: mining social media and applications; natural language processing; data mining; information retrieval; emergent social media processing problems.
This book constitutes the proceedings of the 6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013, held in Washington, DC, USA in April 2013. The total of 57 contributions, which consists of papers and posters, included in this volume was carefully reviewed and selected from 137 submissions. This conference is strongly committed to multidisciplinarity, consistent with recent trends in computational social science and related fields. The topics covered are: behavioral science, health sciences, military science and information science. There are also many papers that provide methodological innovation as well as new domain-specific findings.
This book constitutes the refereed proceedings of the 14th International Conference on Discovery Science, DS 2011, held in Espoo, Finland, in October 2011 - co-located with ALT 2011, the 22nd International Conference on Algorithmic Learning Theory. The 24 revised full papers presented together with 5 invited lectures were carefully revised and selected from 56 submissions. The papers cover a wide range including the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, as well as their application to knowledge discovery.
A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry
This book constitutes the proceedings of the Third International Conference on Internet Science held in Florence, Italy, in September 2016. The 25 papers presented were carefully reviewed and selected for inclusion in this volume. They were organized in topical sections named: collective awareness and crowdsourcing platforms ̧ collaboration, privacy and conformity in virtual/social environments; internet interoperability, freedom and data analysis; smart cities and sociotechnical systems.
The two-volume set LNAI 10061 and 10062 constitutes the proceedings of the 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, held in Cancún, Mexico, in October 2016. The total of 86 papers presented in these two volumes was carefully reviewed and selected from 238 submissions. The contributions were organized in the following topical sections: Part I: natural language processing; social networks and opinion mining; fuzzy logic; time series analysis and forecasting; planning and scheduling; image processing and computer vision; robotics. Part II: general; reasoning and multi-agent systems; neural networks and deep learning; evolutionary algorithms; machine learning; classification and clustering; optimization; data mining; graph-based algorithms; and intelligent learning environments.