Abstract
Social media has become an important platform for people to express opinions, share information and communicate with others. Detecting and tracking topics from social media can help people grasp essential information and facilitate many security-related applications. As social media texts are usually short, traditional topic evolution models built based on LDA or HDP often suffer from the data sparsity problem. Recently proposed topic evolution models are more suitable for short texts, but they need to manually specify topic number which is fixed during different time period. To address these issues, in this paper, we propose a nonparametric topic evolution model for social media short texts. We first propose the recurrent semantic dependent Chinese restaurant process (rsdCRP), which is a nonparametric process incorporating word embeddings to capture semantic similarity information. Then we combine rsdCRP with word co-occurrence modeling and build our short-Text oriented topic evolution model sdTEM. We carry out experimental studies on Twitter dataset. The results demonstrate the effectiveness of our method to monitor social media topic evolution compared to the baseline methods.
| Original language | English (US) |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Intelligence and Security Informatics |
| Subtitle of host publication | Security and Big Data, ISI 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 119-124 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509067275 |
| DOIs | |
| State | Published - Aug 8 2017 |
| Externally published | Yes |
| Event | 15th IEEE International Conference on Intelligence and Security Informatics, ISI 2017 - Beijing, China Duration: Jul 22 2017 → Jul 24 2017 |
Other
| Other | 15th IEEE International Conference on Intelligence and Security Informatics, ISI 2017 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 7/22/17 → 7/24/17 |
Fingerprint
Keywords
- Social Media Analytics
- Text Mining
- Topic Modeling
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
- Information Systems and Management
- Safety, Risk, Reliability and Quality
Cite this
Topic evolution modeling in social media short texts based on recurrent semantic dependent CRP. / Zhang, Yuhao; Mao, Wenji; Zeng, Dajun.
2017 IEEE International Conference on Intelligence and Security Informatics: Security and Big Data, ISI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 119-124 8004885.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Topic evolution modeling in social media short texts based on recurrent semantic dependent CRP
AU - Zhang, Yuhao
AU - Mao, Wenji
AU - Zeng, Dajun
PY - 2017/8/8
Y1 - 2017/8/8
N2 - Social media has become an important platform for people to express opinions, share information and communicate with others. Detecting and tracking topics from social media can help people grasp essential information and facilitate many security-related applications. As social media texts are usually short, traditional topic evolution models built based on LDA or HDP often suffer from the data sparsity problem. Recently proposed topic evolution models are more suitable for short texts, but they need to manually specify topic number which is fixed during different time period. To address these issues, in this paper, we propose a nonparametric topic evolution model for social media short texts. We first propose the recurrent semantic dependent Chinese restaurant process (rsdCRP), which is a nonparametric process incorporating word embeddings to capture semantic similarity information. Then we combine rsdCRP with word co-occurrence modeling and build our short-Text oriented topic evolution model sdTEM. We carry out experimental studies on Twitter dataset. The results demonstrate the effectiveness of our method to monitor social media topic evolution compared to the baseline methods.
AB - Social media has become an important platform for people to express opinions, share information and communicate with others. Detecting and tracking topics from social media can help people grasp essential information and facilitate many security-related applications. As social media texts are usually short, traditional topic evolution models built based on LDA or HDP often suffer from the data sparsity problem. Recently proposed topic evolution models are more suitable for short texts, but they need to manually specify topic number which is fixed during different time period. To address these issues, in this paper, we propose a nonparametric topic evolution model for social media short texts. We first propose the recurrent semantic dependent Chinese restaurant process (rsdCRP), which is a nonparametric process incorporating word embeddings to capture semantic similarity information. Then we combine rsdCRP with word co-occurrence modeling and build our short-Text oriented topic evolution model sdTEM. We carry out experimental studies on Twitter dataset. The results demonstrate the effectiveness of our method to monitor social media topic evolution compared to the baseline methods.
KW - Social Media Analytics
KW - Text Mining
KW - Topic Modeling
UR - http://www.scopus.com/inward/record.url?scp=85030233335&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030233335&partnerID=8YFLogxK
U2 - 10.1109/ISI.2017.8004885
DO - 10.1109/ISI.2017.8004885
M3 - Conference contribution
AN - SCOPUS:85030233335
SP - 119
EP - 124
BT - 2017 IEEE International Conference on Intelligence and Security Informatics
PB - Institute of Electrical and Electronics Engineers Inc.
ER -