Social Media Mining with R 2014

相似文件 换一批

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

1. Product Details
   
2. Paperback: 103 pages
   
3. Publisher: Packt Publishing - ebooks Account (March 24, 2014)
   
4. Language: English
   
5. ISBN-10: 1783281774
   
6. ISBN-13: 978-1783281770
   
7. Product Dimensions: 7.5 x 0.3 x 9.2 inches

复制代码

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏6 回帖

关键词：social media Social Media ning With Media

114页，入门级的R语言社会网络挖掘材料。

114页，入门级的R语言社会网络挖掘材料。

114页，入门级的R语言社会网络挖掘材料。


不好意思，网页迟缓，点多了！！！

帮楼主贴一下此书的TOC，方便广大感兴趣的坛友，:-)

Table of Contents
Preface
Chapter 1: Going Viral Social media mining using sentiment analysis
The state of communication
What is Big Data?
Human sensors and honest signals
Quantitative approaches
Summary

Chapter 2: Getting Started with R Why R?
Quick start The basics – assignment and arithmetic
Functions, arguments, and help
Vectors, sequences, and combining vectors
A quick example – creating data frames and importing files
Visualization in R
Style and workflow
Additional resources
Summary

Chapter 3: Mining Twitter with R Why Twitter data?
Obtaining Twitter data
Preliminary analyses
Summary

Chapter 4: Potentials and Pitfalls of Social Media Data Opinion mining made difficult
Sentiment and its measurement
The nature of social media data
Traditional versus nontraditional social data
Measurement and inferential challenges
Summary

Chapter 5: Social Media Mining – Fundamentals Key concepts of social media mining
Good data versus bad data
Understanding sentiments Scherer's typology of emotions
Sentiment polarity – data and classification
Supervised social media mining – lexicon-based sentiment
Supervised social media mining – Naive Bayes classifiers
Unsupervised social media mining – Item Response Theory for text scaling
Summary

Chapter 6: Social Media Mining – Case Studies Introductory considerations
Case study 1 – supervised social media mining – lexicon-based sentiment
Case study 2 – Naive Bayes classifier
Case study 3 – IRT models for unsupervised sentiment scaling
Summary

Appendix: Conclusions and Next Steps Final thoughts
An expanding field
Further reading
Bibliography
Index

为什么要这么贵

很好的的 啊

为什么这么贵

too bad

这本书写的很浅，实质性内容不多，实在不值这么多论坛币。