Python社交媒体分析实战

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"Python社交媒体分析" 本书《Python Social Media Analytics》由Siddhartha Chatterjee和Michal Krystyanczuk共同撰写，由Packt Publishing于2017年出版。这本书深入探讨了如何利用Python进行社交媒体数据分析，帮助读者理解和掌握在当今大数据时代如何从海量的社交媒体数据中提取有价值的信息。在Python编程语言的支持下，读者可以学习到如何收集、处理、分析以及可视化社交媒体上的数据。Python作为一种强大的脚本语言，因其易学性和丰富的库支持，成为数据分析领域中的首选工具。本书可能涵盖了以下核心知识点： 1. 数据采集：使用Python的库如Scrapy、BeautifulSoup或Requests来抓取社交媒体平台（如Twitter、Facebook、Instagram等）上的公开数据。这包括了解API接口的使用，获取API访问令牌，以及处理数据限制和速率限制。 2. 数据预处理：学习如何清洗和整理抓取的数据，处理缺失值、异常值，以及转换数据格式。Python的Pandas库在这方面非常强大，提供了方便的数据操作和清洗功能。 3. 自然语言处理（NLP）：使用NLTK（自然语言工具包）、TextBlob或spaCy进行文本分析，包括情感分析、关键词提取、主题建模等。这些工具可以帮助理解用户在社交媒体上表达的情绪和观点。 4. 数据分析与挖掘：通过使用NumPy和SciPy进行统计分析，以及使用机器学习库如Scikit-learn进行预测模型构建。例如，可以分析用户的行为模式，预测趋势，或者识别影响话题传播的关键节点。 5. 数据可视化：使用Matplotlib、Seaborn和Plotly等库将分析结果以图表的形式呈现出来，使复杂的数据更容易理解。可视化是传达发现的重要方式，能够帮助读者更好地解释分析结果。 6. 实战项目：书中可能会包含实际案例，如品牌声誉管理、市场研究或舆情监控等，让读者有机会将所学应用到实际情境中。 7. 效率与优化：学习如何使用Dask或Apache Spark等分布式计算框架处理大规模数据，提高分析效率。 8. 隐私和合规性：了解在收集和使用社交媒体数据时应遵循的法规和道德标准，确保数据处理过程的合法性和合规性。《Python Social Media Analytics》是一本全面介绍如何利用Python进行社交媒体数据分析的指南，旨在帮助读者提升数据分析技能，利用社交媒体数据洞察用户行为、市场动态和舆论趋势。通过学习这本书，读者将能够构建自己的社交媒体数据分析系统，为企业决策提供有力支持。

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Preface

Social media in the last decade has taken the world by storm. Billions of interactions take

place around the world among the different users of Facebook, Twitter, YouTube, online

forums, Pinterest, GitHub, and others. All these interactions, either captured through the

data provided by the APIs of these platforms or through custom crawlers, have become a

hotbed of information and insights for organizations and scientists around the world.

Python Social Media Analytics has been written to show the most practical means of

capturing this data, cleaning it, and making it relevant for advanced analytics and insight

hunting. The book will cover basic to advanced concepts for dealing with highly

unstructured data, followed by extensive analysis and conclusions to give sense to all of the

processing.

What this book covers

$IBQUFS, Introduction to the Latest Social Media Landscape and Importance, covers the

updated social media landscape and key figures. We also cover the technical environment

around Python, algorithms, and social networks, which we later explain in detail.

$IBQUFS, Harnessing Social Data - Connecting, Capturing, and Cleaning, introduces methods

to connect to the most popular social networks. It involves the creation of developer

applications on chosen social media and then using Python libraries to make connections to

those applications and querying the data. We take you through the advantages and

limitations of each social media platform, basic techniques to clean, structure, and

normalize the data using text mining and data pre-processing. Finally, you are introduced

to MongoDB and essential administration methods.

$IBQUFS, Uncovering Brand Activity, Emotions, and Popularity on Facebook, introduces the

role of Facebook for brand activity and reputation. We will also introduce you to the

Facebook API ecosystem and the methodology to extract data. You will learn the concepts

of feature extraction and content analysis using keywords, hashtags, noun phrases, and

verbatim extraction to derive insights from a Facebook brand page. Trend analysis on time-

series data, and emotion analysis via the AlchemyAPI from IBM, are also introduced.

Preface

[ 2 ]

$IBQUFS, Analyzing Twitter Using Sentiment Analysis and Entity Recognition, introduces you

to Twitter, its uses, and the methodology to extract data using its REST and Streaming APIs

using Python. You will learn to perform text mining techniques, such as stopword removal,

stemming using NLTK, and more customized cleaning such as device detection. We will

also introduce the concept and application of sentiment analysis using a popular Python

library, VADER. This chapter will demonstrate the classification technique of machine

learning to build a custom sentiment analysis algorithm.

$IBQUFS, Campaigns and Consumer Reaction Analytics on YouTube - Structured and

Unstructured, demonstrates the analysis of both structured and unstructured data,

combining the concepts we learned earlier with newer ones. We will explain the

characteristics of YouTube and how campaigns and channel popularity are measured using

a combination of traffic and sentiment data from user comments. This will also serve as an

introduction to the Google developer platform needed to access and extract the data.

$IBQUFS, The Next Great Technology - Trends Mining on GitHub, introduces you to GitHub,

its API, and characteristics. This chapter will demonstrate how to analyze trends on GitHub

to discover projects and technologies that gather the most interest from users. We use

GitHub data around repositories such as watchers, forks, and open issues to while making

interesting analysis to infer the most emerging projects and technologies.

Chapter 7, Scraping and Extracting Conversational Topics on Internet Forums, introduces public

consumer forums with real-world examples and explains the importance of forum

conversations for extracting insights about people and topics. You will learn the

methodology to extract forum data using Scrapy and BeautifulSoup in Python. We'll apply

the preceding techniques on a popular car forum and use Topic Models to analyze all the

conversations around cars.

$IBQUFS, Demystifying Pinterest through Network Analysis of Users Interests, introduces an

emerging and important social network, Pinterest, along with the advanced social network

analysis concept of Graph Mining. Along with the Pinterest API, we will introduce the

technique of advanced scraping using Selenium. You will learn to extract data from

Pinterest to build a graph of pins and boards. The concepts will help you analyze and

visualize the data to find the most influential topics and users on Pinterest. You will also be

introduced to the concept of community detection using Python modules.

$IBQUFS, Social Data Analytics at Scale - Spark and Amazon Web Services, takes the reader on

a tour of distributed and parallel computing. This chapter will be an introduction to

implementing Spark, a popular open source cluster-computing framework. You will learn

to get Python scripts ready to run at scale and execute Spark jobs on the Amazon Web

Services cloud.

Preface

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Errata

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