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People express their opinions and emotions freely in social media posts andonline reviews that contain valuable feedback for multiple stakeholders such asbusinesses and political campaigns. Manually extracting opinions and emotionsfrom large volumes of such posts is an impossible task. Therefore, automatedprocessing of these posts to extract opinions and emotions is an importantresearch problem. However, human emotion detection is a challenging task due tothe complexity and nuanced nature. To overcome these barriers, researchers haveextensively used techniques such as deep learning, distant supervision, andtransfer learning. In this paper, we propose a novel Pyramid Attention Network(PAN) based model for emotion detection in microblogs. The main advantage ofour approach is that PAN has the capability to evaluate sentences in differentperspectives to capture multiple emotions existing in a single text. Theproposed model was evaluated on a recently released dataset and the resultsachieved the state-of-the-art accuracy of 58.9%.

Gated Recurrent Neural Network Approach for Multilabel Emotion Detection in Microblogs