In this project, we have focused on the Humicroedit data set which was created for one of the tasks presented during SemEval 2020. It contains the micro-edited versions of existing headlines where the aim was to make them funny. To analyze the edited headlines, various NLP techniques were applied, where the goal was to analyze the relationships and trends between the humor scores, sentiments, and topics of each data item. We have also considered the named entities and keywords, although to a lesser extent. This resulted in a visualization prototype that utilized the information visualization techniques treemap and dimensionality reduction. With the help of filtering and exploration features, an analysis could be performed on the data from the perspective of the different NLP modules. For this particular data set it was found that depending on the sentiment module, items of a certain score range and sentiment range will be grouped differently. It could also be determined that the sentiment value and the funniness score were highly dependent on the context of the edited headline. No certain connection could be made on how the topic affected the funniness score or the sentiment value due to the imbalanced distribution of topics in the Humicroedit data set. The prototype was evaluated by experts in NLP research and related fields. They deemed the prototype useful for its purpose and saw potential in exploring similar data sets with it, as well as reusing some of its features in their line of work.