Sentiment analysis is often a crowdsourcing task prone to subjective labels given by many annotators.
User-generated content from social media is produced in many languages, making it technically challenging to compare the discussed themes from one domain across different cultures and regions.
In this paper, we demonstrate how these methods can be used to display correlated topic models on social media texts using SocialVisTUM, our proposed interactive visualization toolkit.
For scenario b) we compare abstract class labels given by the domain expert as baseline and by automatic hierarchical clustering.
A major challenge in modern neural networks is the utilization of previous knowledge for new tasks in an effective manner, otherwise known as transfer learning.