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Network Analysis on Twitter Streams Open Details

Create a python script about:

1. Using the following tweets dataset, identify the most influential users using the PageRank algorithm:

Link: [login to view URL] (Files from 2020/01 to 2020/05)

See attached file for detail.

List the 100 most influential users (user id, user name, PageRank score).

PageRank example implementation script: [login to view URL]

Notes:

You should use the Spark GraphX or GraphFrames library.

Limit your analysis to tweets in Portuguese. (Language filter)

To create the network, create an edge pointing from a retweeter to the user who tweeted.

2. Process a Twitter stream to identify the most popular users within a time window.

a) Continually collect the stream using the filter endpoint of the Twitter Streaming API to select

tweets from the United Kingdom.

Suggestion: Use the bounding box [-8.6, 49.5, 1.46, 60.5]

b) Apply the exponentially decaying window approach to keep smoothed counts of mentions in the

collected tweets, and display the 10 most popular users in (user-defined) intervals of t seconds.

See example tweets and output (10 sec window; some outputs removed) below.

c) Apply a sentiment classifier to the collected tweets, as they arrive, and keep two counts

to track the most popular and unpopular users

Skills: Python, Machine Learning (ML), Spark, Data Mining, Data Analysis

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