Detecting Emotions in Social Media: A Constrained Optimization Approach
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 996-1001.
Abstract
Emotion detection can considerably enhance our understanding of users' emotional states. Understanding users' emotions especially in a real-time setting can be pivotal in improving user interactions and understanding their preferences. In this paper, we propose a constraint optimization framework to discover emotions from social media content of the users. Our framework employs several novel constraints such as emotion bindings, topic correlations, along with specialized features proposed by prior work and well-established emotion lexicons. We propose an efficient inference algorithm and report promising empirical results on three diverse datasets.
@inproceedings{ijcai2015p145,
title = {Detecting Emotions in Social Media: A Constrained Optimization Approach},
author = {Wang, Yichen and Pal, Aditya},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {996--1001},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/145},
}
TY - CPAPER TI - Detecting Emotions in Social Media: A Constrained Optimization Approach AU - Wang, Yichen AU - Pal, Aditya T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 996 EP - 1001 UR - https://ijcai.org/proceedings/2015/145 ER -Copied
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