  Speech is the most common way humans express their feelings, and sentiment
analysis is the use of tools such as natural language processing and
computational algorithms to identify the polarity of these feelings. Even
though this field has seen tremendous advancements in the last two decades, the
task of effectively detecting under represented sentiments in different kinds
of datasets is still a challenging task. In this paper, we use single and
bi-modal analysis of short dialog utterances and gain insights on the main
factors that aid in sentiment detection, particularly in the underrepresented
classes, in datasets with and without inherent sentiment component.
Furthermore, we propose an architecture which uses a learning rate scheduler
and different monitoring criteria and provides state-of-the-art results for the
SWITCHBOARD imbalanced sentiment dataset.
