  People express their opinions and emotions freely in social media posts and
online reviews that contain valuable feedback for multiple stakeholders such as
businesses and political campaigns. Manually extracting opinions and emotions
from large volumes of such posts is an impossible task. Therefore, automated
processing of these posts to extract opinions and emotions is an important
research problem. However, human emotion detection is a challenging task due to
the complexity and nuanced nature. To overcome these barriers, researchers have
extensively used techniques such as deep learning, distant supervision, and
transfer learning. In this paper, we propose a novel Pyramid Attention Network
(PAN) based model for emotion detection in microblogs. The main advantage of
our approach is that PAN has the capability to evaluate sentences in different
perspectives to capture multiple emotions existing in a single text. The
proposed model was evaluated on a recently released dataset and the results
achieved the state-of-the-art accuracy of 58.9%.
