As we covered in our recent NLP blog, there are a lot of cool use cases for text / sentiment analysis. One recent instance we found really interesting came out of our May presentation at SeaTUG (Seattle Tableau User Group.) As part of our presentation / demo we decided to find out what some of the local Tableau users could do with trial access to Keboola; below we’ll highlight what Hong Zhu and a group of students from the University of Washington were able to accomplish with Keboola + Tableau for a class final project!
What class was this for and why did you want to do this for a final project?
We are a group of students at the University of Washington’s department of Human Centered Design and Engineering. For our class project for HCDE 511 – Information Visualization, we made an interactive tool to visualize music data from Last FM. We chose the topic of music because all 4 of us are music lovers.
Initially, the project was driven by our interest in having an international perspective on the popularity vs. obscurity of artists and tracks. However, after interviewing a number of target users, we learned that most of them were not interested in rankings in other countries. In fact, most of them were not interested in the ranking of artists/tracks at all. Instead, our target users were interested in having more individualized information and robust search functions, in order to quickly find the right music that is tailored to one’s taste, mood, and occasion. Therefore, we re-focused our efforts on parsing out the implicit attributes, such as genre and sentiment, from the 50 most-used tags of each track. That was when Keboola and its NLP plug-in came into play and became instrumental in the success of this project.