挖掘社交网络第2版

图书标准信息

• 作者 [美]罗塞尔（Matthew A.Russell） 著
• 出版社 东南大学出版社
• 出版时间 2014-10
• 版次 1
• ISBN 9787564150051
• 定价 78.00元
• 装帧 平装
• 开本 16开
• 纸张 胶版纸
• 页数 421页
• 字数 548千字
• 正文语种 英语

【内容简介】

　　《挖掘社交网络（第2版影印版）》中简洁而且具有操作性的书将为你展示如何回答这些甚至更多的问题，你将学到如何组合社交网络数据、分析技术，如何通过可视化帮助你找到你一直在社交世界中的内容。《挖掘社交网络（第2版影印版）》中每个独立章节介绍了在社交网络的不同领域挖掘数据的技术，这些领域包括博客和电子邮件。你所需要具备的就是一定的编程经验和学习基本的python工具的意愿。

【目录】

Preface
PartⅠ.AGuidedTouroftheSociaIWeb
Prelude
1.MiningTwitter：ExploringTrendingTopics，DiscoveringWhatPeopleAreTalkingAbout，andMore
1.1.Overview
1.2.WhyIsTwitterAlltheRage？
1.3.ExploringTwitter'sAPI
1.3.1.FundamentalTwitterTerminology
1.3.2.CreatingaTwitterAPIConnection
1.3.3.ExploringTrendingTopics
1.3.4.SearchingforTweets
1.4.Analyzingthe140Characters
1.4.1.ExtractingTweetEntities
1.4.2.AnalyzingTweetsandTweetEntitieswithFrequencyAnalysis
1.4.3.ComputingtheLexicalDiversityofTweets
1.4.4.ExaminingPatternsinRetweets
1.4.5.VisualizingFrequencyDatawithHistograms
1.5.ClosingRemarks
1.6.RecommendedExercises
1.7.OnlineResources
2.MiningFacebook：AnalyzingFanPages，ExaminingFriendships，andMore
2.1.Overview
2.2.ExploringFacebook'sSocialGraphAPI
2.2.1.UnderstandingtheSocialGraphAPI
2.2.2.UnderstandingtheOpenGraphProtocol
2.3.AnalyzingSocialGraphConnections
2.3.1.AnalyzingFacebookPages
2.3.2.ExaminingFriendships
2.4.ClosingRemarks
2.5.RecommendedExercises
2.6.OnlLneResources
3.MiningLinkedIn：FacetingJobTrtles，ClusteringColleagues，andMore
3.1.Overview
3.2.ExploringtheLinkedlnAPI
3.2.1.MakingLinkedlnAPIRequests
3.2.2.DownloadingLinkedlnConnectionsasaCSVFile
3.3.CrashCourseonClusteringData
3.3.1.ClusteringEnhancesUserExperiences
3.3.2.NormalizingDatatoEnableAnalysis
3.3.3.MeasuringSimilarity
3.3.4.ClusteringAlgorithms
3.4.ClosingRemarks
3.5.RecommendedExerases
3.6.OnlineResources
4.MiningGoogleComputingDocumentSimilarity，ExtractingCollocations，andMore
4.1.Overview
4.2.ExploringtheGoogle+API
4.2.1.MakingGoogle+APIRequests
4.3.AWhiz—BangIntroductiontoTF—IDF
4.3.1.TermFrequency
4.3.2.InverseDocumentFrequency
4.3.3.TF—IDF
4.4.QueryingHumanLanguageDatawithTF—IDF
4.4.1.IntroducingtheNaturalLanguageToolkit
4.4.2.ApplyingTF—IDFtoHumanLanguage
4.4.3.FindingSimilarDocuments
4.4.4.AnalyzingBigramsinHumanLanguage
4.4.5.ReflectionsonAnalyzingHumanLanguageData
4.5.ClosingRemarks
4.6.RecommendedExercises
4.7.OnlineResources
5.MiningWebPages：UsingNaturalLanguageProcessingtoUnderstandHumanLanguage，SummarizeBlogPosts，andMore.
5.1.Overview
5.2.Scraping，Parsing，andCrawlingtheWeb
5.2.1.Breadth—FirstSearchinWebCrawling
5.3.DiscoveringSemanticsbyDecodingSyntax
5.3.1.NaturalLanguageProcessingIllustratedStep—by—Step
5.3.2.SentenceDetectioninHumanLanguageData
5.3.3.DocumentSummarization
5.4.Entity—CentricAnalysis：AParadigmShift
5.4.1.GistingHumanLanguageData
5.5.QualityofAnalyticsforProcessingHumanLanguageData
5.6.ClosingRemarks
5.7.RecommendedExercises
5.8.OnlineResources
6.MiningMailboxes：AnalyzingWho'sTalkingtoWhomAboutWhat，HowOften，andMore
6.1.Overview
6.2.ObtainingandProcessingaMailCorpus
6.2.1.APrimeronUnixMailboxes
6.2.2.GettingtheEnronData
6.2.3.ConvertingaMailCorpustoaUnixMailbox
6.2.4.ConvertingUnixMailboxestoJSON
6.2.5.ImportingaJSONifiedMailCorpusintoMongoDB
6.2.6.ProgrammaticallyAccessingMongoDBwithPython
6.3.AnalyzingtheEnronCorpus
6.3.1.QueryingbyDate／TimeRange
6.3.2.AnalyzingPatternsinSender／RecipientCommunications
6.3.3.WritingAdvancedQueries
6.3.4.SearchingEmailsbyKeywords
6.4.DiscoveringandVisualizingTime—SeriesTrends
6.5.AnalyzingYourOwnMailData
6.5.1.AccessingYourGmailwithOAuth
6.5.2.FetchingandParsingEmailMessageswithIMAP
6.5.3.VisualizingPatternsinGMailwiththe"GraphYourInboxChromeExtension
6.6.ClosingRemarks
6.7.RecommendedExercises
6.8.OnlineResources
7MiningGitHub：lnspectingSoftwareCollaborationHabits，BuildingInterestGraphs，andMore
7.1.Overview
7.2.ExploringGitHub'sAPI
7.2.1.CreatingaGitHubAPIConnection
7.2.2.MakingGitHubAPIRequests
7.3.ModelingDatawithPropertyGraphs
7.4.AnalyzingGitHubInterestGraphs
7.4.1.SeedinganInterestGraph
7.4.2.ComputingGraphCentralityMeasures
7.4.3.ExtendingtheInterestGraphwith"Follows"EdgesforUsers
7.4.4.UsingNodesasPivotsforMoreEfflcientQueries
7.4.5.VisualizingInterestGraphs
7.5.ClosingRemarks
7.6.RecommendedExercises
7.7.OnlineResources
8.MiningtheSemanticallyMarked—UpWeb：ExtractingMicroformats，lnferencingoverRDF，andMore.
8.1.Overview
8.2.Microformats：Easy—to—ImplementMetadata
8.2.1.Geocoordinates：ACommonThreadforJustAboutAnything
8.2.2.UsingRecipeDatatoImproveOnlineMatchmaking
8.2.3.AccessingLinkedln's200MillionOnlineResumes
8.3.FromSemanticMarkuptoSemanticWeb：ABriefInterlude
8.4.TheSemanticWeb：AnEvolutionaryRevolution
8.4.1.ManCannotLiveonFactsAlone
8，4.2.InferencingAboutanOpenWorld
8.5.ClosingRemarks
8.6.RecommendedExercises
8.7.OnlineResources

PartⅡ.Twitter（ookbook
9.TwitterCookbook
9.1.AccessingTwitter'sAPIforDevelopmentPurposes
9.2.DoingtheOAuthDancetoAccessTwitter'sAPIforProductionPurposes
9.3.DiscoveringtheTrendingTopics
9.4.SearchingforTweets
9.5.ConstructingConvenientFunctionCalls
9.6.SavingandRestoringJSONDatawithTextFiles
9.7.SavingandAccessingJSONDatawithMongoDB
9.8.SamplingtheTwitterFirehosewiththeStreamingAPI
9.9.CollectingTime—SeriesData
9.10.ExtractingTweetEntities
9.11.FindingtheMostPopularTweetsinaCollectionofTweets
9.12.FindingtheMostPopularTweetEntitiesinaCollectionofTweets
9.13.TabulatingFrequencyAnalysis
9.14.FindingUsersWhoHaveRetweetedaStatus
9.15.ExtractingaRetweet'sAttribution
9.16.MakingRobustTwitterRequests
9.17.ResolvingUserProfileInformation
9.18.ExtractingTweetEntitiesfromArbitraryText
9.19.GettingAllFriendsorFollowersforaUser
9.20.AnalyzingaUser'sFriendsandFollowers
9.21.HarvestingaUser'sTweets
9.22.CrawlingaFriendshipGraph
9.23.AnalyzingTweetContent
9.24.SummarizingLinkTargets
9.25.AnalyzingaUser'sFavoriteTweets
9.26.ClosingRemarks
9.27.RecommendedExercises
9.28.OnlineResources

PartⅢ.Appendixes
A.InformationAboutThisBook'sVirtualMachineExperience
B.OAuthPrimer
C.PythonandIPythonNotebookTips&Tricks
Index.