000			04411cam a2200301zu 4500
001			88842824
003			FRCYB88842824
005			20250107215757.0
006			m o d
007			cr un
008			250107s2017 fr | o|||||0|0|||eng d
020			_a9781787127524
035			_aFRCYB88842824
040			_aFR-PaCSA _ben _c _erda
100	1		_aBali, Raghav
245	0	1	_aLearning Social Media Analytics with R _c['Bali, Raghav', 'Sarkar, Dipanjan', 'Sharma, Tushar']
264		1	_bPackt Publishing _c2017
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aBali, Raghav
700	0		_aSarkar, Dipanjan
700	0		_aSharma, Tushar
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88842824 _qtext/html _a
520			_aTap into the realm of social media and unleash the power of analytics for data-driven insights using RAbout This BookA practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media dataLearn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms.Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering.Who This Book Is ForIt is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise.What You Will LearnLearn how to tap into data from diverse social media platforms using the R ecosystemUse social media data to formulate and solve real-world problemsAnalyze user social networks and communities using concepts from graph theory and network analysisLearn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channelsUnderstand the art of representing actionable insights with effective visualizationsAnalyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so onLearn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many moreIn DetailThe Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data.The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights.Style and approachThis book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so on.
999			_c65406 _d65406