  Sentiment prediction remains a challenging and unresolved task in various
research fields, including psychology, neuroscience, and computer science. This
stems from its high degree of subjectivity and limited input sources that can
effectively capture the actual sentiment. This can be even more challenging
with only text-based input. Meanwhile, the rise of deep learning and an
unprecedented large volume of data have paved the way for artificial
intelligence to perform impressively accurate predictions or even human-level
reasoning. Drawing inspiration from this, we propose a coverage-based sentiment
and subsentence extraction system that estimates a span of input text and
recursively feeds this information back to the networks. The predicted
subsentence consists of auxiliary information expressing a sentiment. This is
an important building block for enabling vivid and epic sentiment delivery
(within the scope of this paper) and for other natural language processing
tasks such as text summarisation and Q&A. Our approach outperforms the
state-of-the-art approaches by a large margin in subsentence prediction (i.e.,
Average Jaccard scores from 0.72 to 0.89). For the evaluation, we designed
rigorous experiments consisting of 24 ablation studies. Finally, our learned
lessons are returned to the community by sharing software packages and a public
dataset that can reproduce the results presented in this paper.
