The noise in the data:¶

Once you have obtained the data that we want, the next major step is to pre-process this data. Most of the textual data that we get from social media has a lot of noise. We cannot use this data stright awawy. We need to remove this noise before we try to get any meaningful insights from this data and do some magic with it. The noise can be due to the usage of colloqual language, encoding issues etc.

Table of Contents¶

Introduction:¶

Twitter is one of the ten most visited web sites around the world. Its a microblogging platform that allows you share messages of lenght not more than 140 characters. Not just that , but it also helps you discover messages related to the topics you are intrested in. It can be usefull in many ways. Since its one of the most used websites, there is a lot of data flowing through its network which could used for varius research purposes. Fourtunately twitter decided to share this huge data with some limitations ofcourse to the public. Twitter offers API (Application Programing Interface) to do this. In this tutorial we will be exploring Twitter API and what it has to offer and how we can use some of these to obtain the data that we want.

JSON¶

JSON is a data format that is used for storing or exchanging information and exchanging information. But JSON is human readable and easy to parse. And most importantly it is structured.

A simple example of json:

{
  "name": "jason",
  "age": 24,
  "gender":"male"
}

Fundamentally a json can be built using two things:

Sentiment Analysis:¶

The whole idea of text mining is about gaining insights in textual data. Sentiment anaysis is one of the important applications in the area of text mining. It tries to identify weather the opinoin expressed in a text is positive, negitive or netural towards a given topic.

For example,

1. I am happy about my promotion
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