In this study, we focus on a visual analysis of text data
originally annotated for the tasks of computational humor
analysis, specifically the Humicroedit data set.
This data set was used as part of the shared task at the
Semantic Evaluation workshop.
The data set consists of news headlines that were edited with
substitutions with the intention of making the edited headlines
funnier.
The actual level of funniness of the edited headlines was
individually assessed by five annotators on the scale from
zero (not funny) to three (funny).
The original shared task focused on the development of natural
language processing methods for predicting or ranking such
edited headlines with respect to funniness.
However, our focus in this study is not to develop such an NLP
model, but rather to investigate the respective annotated data in
depth.
For that purpose, we have designed the pipeline that
combines several stages and methods.
The original Humicroedit training data is stored as a CSV
file.
We preprocess it to form edited headline strings.
Then we apply several NLP analyses to identify sentiment
polarity, topics, keywords, and named entities in the text data
and store the results in another CSV file.
All these results can then be loaded and explored in our
interactive visual interface.
The user interface of our prototype consists of several
panels, with the left panel including the file picker
controls for selecting the computational results available
in the provided data, such as the VADER or RoBERTa-based
sentiment analysis results and filtering controls.
The range sliders allow the users to filter a data subset
with specific annotated funniness scores or computed
sentiment polarity scores for the edited headlines.
The prototype also provides an overview of the list of computed
topics as well as the option to filter the respective results in
the central panel.
The prototype provides the main view, which is currently set to
a treemap, but could be switched to a dimensionality
reduction plot.
A detailed data table is located below the main view.
The secondary view is available in the right panel, followed by
several histogram plots focusing on the average annotated
funniness score, sentiment, and topic membership for the
currently visible filtered data set, as well as a specific
selection.
Most of the views support details on demand with tool
tips.
For instance, the tooltips for individual cells within the main
treemap view provide details for the respective headlines.
After clicking on one of such treemap cells, the data table is
populated, including the original headline, for which no
funniness score is provided in the data as it only contains
annotations for edited headlines.
Notice that the treemap cells corresponding to the related
edited headline variants are highlighted with a different
color.
Clicking to select an intermediate node of the treemap
will result in selecting the respective data subset.
By exploring the patterns of the annotated funniness score
distribution versus the sentiment polarity detected in
the respective original and edited headlines, the topics,
and also further analyses such as named entities and keywords,
the user gains insight into the relationship between these
facets of the text with the notion of funniness that the
respective annotators followed.
Thank you for watching this demonstration.