Abstract:
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 textbased
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.
