  A common practice in Natural Language Processing (NLP) is to visualize the
text corpus without reading through the entire literature, still grasping the
central idea and key points described. For a long time, researchers focused on
extracting topics from the text and visualizing them based on their relative
significance in the corpus. However, recently, researchers started coming up
with more complex systems that not only expose the topics of the corpus but
also word closely related to the topic to give users a holistic view. These
detailed visualizations spawned research on comparing text corpora based on
their visualization. Topics are often compared to idealize the difference
between corpora. However, to capture greater semantics from different corpora,
researchers have started to compare texts based on the sentiment of the topics
related to the text. Comparing the words carrying the most weightage, we can
get an idea about the important topics for corpus. There are multiple existing
texts comparing methods present that compare topics rather than sentiments but
we feel that focusing on sentiment-carrying words would better compare the two
corpora. Since only sentiments can explain the real feeling of the text and not
just the topic, topics without sentiments are just nouns. We aim to
differentiate the corpus with a focus on sentiment, as opposed to comparing all
the words appearing in the two corpora. The rationale behind this is, that the
two corpora do not many have identical words for side-by-side comparison, so
comparing the sentiment words gives us an idea of how the corpora are appealing
to the emotions of the reader. We can argue that the entropy or the
unexpectedness and divergence of topics should also be of importance and help
us to identify key pivot points and the importance of certain topics in the
corpus alongside relative sentiment.
