Why analyze Twitter, how to access Twitter APIs, and understanding Twitter JSON.

Analyzing Twitter data

Why Analyze Twitter Data?

Uses of Twitter analysis

Collecting data through the Twitter API

Twitter APIs

Setting up tweepy authentication

Collecting data on keywords

Understanding Twitter JSON

Loading and accessing tweets

Accessing user data

Accessing retweet data

Basics of Analyzing Twitter Data

Processing Twitter text

Tweet Items and Tweet Flattening

A tweet flattening function

Loading tweets into a DataFrame

Counting words

Finding keywords

Looking for text in all the wrong places

Comparing #python to #rstats

Time series

Creating time series data frame

Generating mean frequency

Plotting mean frequency

Sentiment analysis

Loading VADER

Calculating sentiment scores

Plotting sentiment scores

Twitter networks

Types of Twitter networks

Which direction is the arrow?

Importing and visualizing Twitter networks

Creating retweet network

Creating reply network

Visualizing retweet network

Individual-level network metrics

In-degree centrality

Betweenness centrality

Ratios

Twitter Networks

Maps and Twitter data

Motivations

Comparisons

Geographical data in Twitter JSON

Coordinates and bounding boxes

Accessing user-defined location

Accessing bounding box

Calculating the centroid

Creating Twitter maps

Creating Basemap map

Plotting centroid coordinates

Coloring by sentiment

Congratulations!

Putting Twitter data on the map

Data Science Hashtag dataset

State of the Union Reply Network dataset

State of the Union Retweet Networking dataset

Twitter produces hundreds of million messages per day, with people around the world discussing sports, politics, business, and entertainment. You can access thousands of messages flowing in this stream in a matter of minutes. In this course, you will learn how to collect Twitter data and analyze tweet text, Twitter networks, and the geographical origin of the tweet. We'll be doing this with datasets on tech companies, data science hashtags, and the 2018 State of the Union address. Using these methods, you will be able to inform business and political decision-making by discovering the prevalence of important topics, the diversity of discussion networks, and a topic's geographical reach.

Data Manipulation with pandas

In this course, you'll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.

Analyzing Social Media Data in Python

Marketinganalyse in Python

Types of Twitter networks

Analyzing Social Media Data in Python

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