Abstract
With the increasing amount of data being published on the Web, it is difficult to analyze their content within a short time. Topic modeling techniques can summarize textual data that contains several topics. Both the label (such as category or tag) and word co-occurrence play a significant role in understanding textual data. However, many conventional topic modeling techniques are limited to the bag-of-words assumption. In this paper, we develop a probabilistic model called Bigram Labeled Latent Dirichlet Allocation (BL-LDA), to address the limitation of the bag-of-words assumption. The proposed BL-LDA incorporates the bigram into the Labeled LDA (L-LDA) technique. Extensive experiments on Yelp data show that the proposed scheme is better than the L-LDA in terms of accuracy.
Original language | English |
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Title of host publication | Proceedings - 2015 International Conference on Computational Science and Computational Intelligence, CSCI 2015 |
Editors | Quoc-Nam Tran, Leonidas Deligiannidis, Hamid R. Arabnia |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 83-88 |
Number of pages | 6 |
ISBN (Electronic) | 9781467397957 |
DOIs | |
Publication status | Published - 2016 Mar 2 |
Event | International Conference on Computational Science and Computational Intelligence, CSCI 2015 - Las Vegas, United States Duration: 2015 Dec 7 → 2015 Dec 9 |
Publication series
Name | Proceedings - 2015 International Conference on Computational Science and Computational Intelligence, CSCI 2015 |
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Other
Other | International Conference on Computational Science and Computational Intelligence, CSCI 2015 |
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Country/Territory | United States |
City | Las Vegas |
Period | 15/12/7 → 15/12/9 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
Keywords
- Data Analysis
- Data Mining
- Text Classification
- Topic Modeling
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Artificial Intelligence
- Computer Networks and Communications
- Hardware and Architecture
- Signal Processing