How to Use Twitter for Data Mining

Data mining is the process of extracting data from data sources, and analyzing it with the intention of finding trends and patterns that can be utilized for decision making. It has a number of advantages, and is therefore used by many organizations of the modern world. In order to make the most out of data mining, companies are hiring employees who have done data analysis certification and are adept in analyzing data.

The five major benefits of data mining for an organization are as follows:

  1. 1. Helps in achieving higher number of satisfied customers and increases customer loyalty
  2. 2. Reveals hidden profitability and helps in overcoming risk factors
  3. 3. Reduces involvement of clients by extracting and analyzing client data
  4. 4. Helps in identifying customer groups, so you can market different products according to the niche
  5. 5. Helps in website optimization by identifying important aspects that should be highlighted and weeding out irrelevant data 

Data Mining Twitter for Predicting Trends

Twitter is a global social media platform and it is nothing less than a goldmine when it comes to data and information. Nearly all tweets are public and easily extractable, which makes it easy to gather large amount of data from twitter for analysis. The fact that twitter data is very specific makes it extremely good for prediction purposes.

The following is an example, in which twitter has been used for data mining:

If you want to analyze the perception of your company amongst people, you can start by collecting tweets and run a sentiment analysis algorithm over it. The following is a guide that you can use for extracting information from twitter

Tools Required

Coding Language: Python 2.7

IDE to write this code: PyCharm - Community Edition.

Python library: Tweepy 

Steps

  1. 1. Create a Twitter account at https://apps.twitter.com/
  2. 2.Create a new app by clicking on the button on the top right
  3. 3. Complete the app creation page by filling in a unique name, a website name, and a project description
  4. 4. After creating the app, click on “Keys and Access Tokens” tab. You will now see your consumer secret and consumer key.
  5. 5. A pair of access tokens will also be needed, so scroll down and request those tokens. The page will refresh, and you will now have an access token and access token secret.
  6. 6. Install Tweepy for accessing the Twitter API
  7. 7. The baseline of each application that can be built for twitter data mining requires Tweepy for creating an API object, so import Tweepy and add your own authentication information
  8. 8. Create API object, which will be the basis of every application

Once you have created the framework, you can find:

  • - Tweets using keywords       
  • - Tweets using the name of the handle
  • - Tweets using timeline

You can use the gathered information for:

  • - Running sentiment analysis
  • - Creating a social graph of the most popular users
  • - Creating a spatial graph on where your organization’s name is mentioned 

Need For Self-Service Business Intelligence

The steps mentioned above can only be followed by people with technical expertise in Python. Organizations have to invest heavily on individuals with relevant experience in coding, which increases the operating cost of a company. Many solutions have been introduced for making data mining simple but none have been as effective as SSBI.

Self-service business intelligence (SSBI) is a form of data analytics specifically devised for business users, who do not have a background in statistical analysis, data mining, or business intelligence. It allows individual with no coding experience to access and work with corporate data. 

Making Data Mining Simple with Power BI by Microsoft

Power BI by Microsoft is a cloud-based business analytics service which makes data mining easy. It allows a person to experience any data through simple drag-and-drop mechanism. It is different from other dashboard solutions as it works with moving charts and live dashboards. It is also able to update visualizations for the purpose of keeping an eye on live streams from supported data sources.

Professionals with expertise in analyzing data are required by organizations for ensuring progress. If you are on the lookout for individuals who are up to the task, you should look at people who have done data analysis certification from a reputed institute like QuickStart. With its amazing corporate training plans and rich experience in IT training, it is the perfect place for learning the latest technologies.

About The Author
Ilya
Account Manager at QuickStart

Ilya Piyevsky

Ilya is a passionate and relationship orientated sales professional. He believes in leveraging past experiences into effective strategies to help IT teams stay current with best practices, while supporting career and knowledge development. As an Account Manager at Quickstart Technologies, he takes a diligent approach to help his clients achieve their training goals, maximize buying power while completing projects on time and within budget.