Twitter data analysis from MongoDB – part 2, Exploring data

In the previous post, I’ve introduced the topic and technology. Now, it’s time to define the problem and methods. Next entries will discuss how to access to MongoDB and how to retrieve geocoded Tweets. I will focus on tweets that are somehow related to the weather using the simplest approach possible – querying their content for the keyword ‘weather’. I will create some nice visualizations later on, an animated gif and a wordcloud that can help us understand what is behind the scenes. You’ll find some code snippets and screenshots so feel free to scroll down to those if you’re not interested in long discussions. So, let’s grab the data from MongoDB and see what’s inside! There’s quite much to do.

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Twitter data analysis from MongoDB – part 1, Introduction

Twitter, MongoDB and PostgreSQL are fun. Let’s put all together and see what we can do. Twitter is an evolving platform for many kinds of analyses. Anyone can access to the content and can be a data scientist for a while. If you’d like to play Big Brother just go ahead and start playing with it and you’ll find a lot of interesting things from people all over the world. Some says that NoSQL databases (such as MongoDB) are perfect for storing Big Data due its scalability and non-relational nature. The good thing in not being a computer scientist is that I can test them as an outsider – without knowing what I am really doing :).

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A little background: a few months before I had access to a database of approx. 200.000 tweets. It’s really nothing compared to some other databases but still big enough for retrieving data to be time-consuming. I was not responsible for the data collection but all data were coming from the Twitter Streaming API and my colleagues stored them both in a MongoDB collection and in a PostgreSQL table. They used the API’s location parameters for requesting data from an area located in the Southern part of the UK. Retrieving geographic data from PostgreSQL (with postgis) is relatively easy and well known but what about MongoDB? Can we even do it? I had no idea but it seemed to be fun enough to explore it. In these posts (maybe there will be 3 or so) I’ll show you how I visualized them. I’ll write about how I tried to extract some weather related information from them (come on, it’s the UK so I thought everyone tweets about the weather!) and lastly, I will show you how I tried to compare the two database engines in terms of speed.

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First off

During the past few months some people told me that I should start blogging about my experiments. Actually, this idea was always in the air but I guess I just never made a lot of effort to think about it seriously. I always got stuck in the very beginning so I haven’t even chose a name for it, not to mention writing posts. Anyway, who the hell is curious about what I think or do? – I always thought. Now, it seems like some people are. Let me briefly explain what one can expect from me.

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