July 2017
Beginner to intermediate
312 pages
7h 27m
English
Content preview from Python Social Media Analytics
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Data analysis
As the data is ready, we can start our analysis. We'll explore time-series analysis for analyzing our dataset using the features that we have extracted. Our goal is to answer the following questions:
- How does sentiment evolve over time? Are there any peaks?
- What are the periods when users comment more?
- What is the day of week when users put the most positive comments?
- What is the day of week when they comment most actively?
In order to answer these questions, we need to work on data in time. Pandas dataframe structures have many interesting properties for time series handling. In previous steps, we have changed the index in our data frame by setting it to the datetime object. We can now use it to resample our data according ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.