Emotion Detection in Online Social Networks: A Multi-label Learning Approach
Emotion Detection in Online Social Networks: A Multi-label Learning Approach
IEEE BASE PAPER ABSTRACT:
Emotion detection in online social networks (OSNs) can benefit kinds of applications such as personalized advertisement services, recommendation systems, etc. Conventionally, emotion analysis mainly focuses on the sentence level polarity prediction or single emotion label classification, however, ignoring the fact that emotions might co-exist from users’ perspective. To this end, in this work, we address the multiple emotions detection in OSNs from user level view, and formulate this problem as a multi-label learning problem. First, we discover emotion labels correlations, social correlations, and temporal correlations from an annotated Twitter dataset. Second, based on the above observations, we adopt a factor graph based emotion recognition model to incorporate emotion labels correlations, social correlations and temporal correlations into a general framework, and detect the multiple emotions based on multi-label learning approach. Performance evaluation demonstrates that the factor graph based emotion detection model can outperform the existing baselines.
PROJECT OUTPUT VIDEO:
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
- System : Pentium i3 Processor
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 2 GB
SOFTWARE REQUIREMENTS:
- Operating System : Windows 10 / 11.
- Coding Language : JAVA.
- Frontend : JSP, HTML, CSS, JavaScript.
- IDE Tool : NetBeans IDE 8.2.
- Database : MYSQL.
REFERENCE:
Xiao Zhang, Wenzhong Li, Member, IEEE, Haochao Ying, Feng Li, Siyi Tang, and Sanglu Lu, Member, IEEE, “Emotion Detection in Online Social Networks: A Multi-label Learning Approach”, IEEE 2020.
