Data Analysis of Music Festival Tweets

Analysis of events significant for the audience, visualization of listener data, user activity charts, comparison of festivals and artists in popularity.


IBM jStart (a concern of the IBM Software Solutions Group) contacted Axmor with the aim of gathering tweets related to music festivals, subsequently providing data analysis, identification of certain patterns and breakdowns of visual analytics.

The outcome — the BlueMix Insights service and IBM Watson analytical services capabilities demonstration.


Over the course of three months, the Axmor team aggregated and analyzed over 2000 tweets focusing on the Coachella festival and similar music events, in addition to popular artists from our client's list. The data was collected using the BlueMix Insights service. The BlueMix Watson Tone Analyzer tool was implemented in order to analyze message tonality.

Utilizing machine learning technology, Axmor developed a trigger driven model which classifies determined tweets.

IBM Twitter Insights
IBM Cloud Object Storage
Jupyter Notebook
Apache Spark
Watson Tone Analyzer
Watson Personality Insights
Alchemy API
Database Design

Utilizing the BlueMix Insights service, the Axmor team gathered all the tweets within a twelve month period, in which festivals or relevant artists were mentioned.

Data Analysis and Visualisation

Spark Notebook and Jupyter Notebook in Python were used in the data mining process.

Data Classification and Analysis Model

Incorporating machine learning technologies, Axmor developers designed a trigger driven data model for the classification of tweets.

Music Festivals and Artists Comparison

Axmor analysed the most popular festivals and gathered data relating to various events influencing the amount of discourse surrounding an artist.

Messages Tonality

To evaluate the success of a given event, Axmor developers used the BlueMix Watson Tone Analyzer tone analysis service.

Provided value
The outcome — the Axmor team was able to identify the most significant events for the audience, compare the popularity of various festivals and artists, visualize user data, create user activity charts based on particular events such as upcoming festivals or album release announcements.
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