  Microblog, an online-based broadcast medium, is a widely used forum for
people to share their thoughts and opinions. Recently, Emotion Recognition (ER)
from microblogs is an inspiring research topic in diverse areas. In the machine
learning domain, automatic emotion recognition from microblogs is a challenging
task, especially, for better outcomes considering diverse content. Emoticon
becomes very common in the text of microblogs as it reinforces the meaning of
content. This study proposes an emotion recognition scheme considering both the
texts and emoticons from microblog data. Emoticons are considered unique
expressions of the users' emotions and can be changed by the proper emotional
words. The succession of emoticons appearing in the microblog data is preserved
and a 1D Convolutional Neural Network (CNN) is employed for emotion
classification. The experimental result shows that the proposed emotion
recognition scheme outperforms the other existing methods while tested on
Twitter data.
