@inproceedings{d4e74a90b9d849fd9513bc1967de0588,
title = "BL-LDA: Bringing bigram to supervised topic model",
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.",
keywords = "Data Analysis, Data Mining, Text Classification, Topic Modeling",
author = "Youngsun Park and Alam, {Md Hijbul} and Ryu, {Woo Jong} and Sang-Geun Lee",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; International Conference on Computational Science and Computational Intelligence, CSCI 2015 ; Conference date: 07-12-2015 Through 09-12-2015",
year = "2016",
month = mar,
day = "2",
doi = "10.1109/CSCI.2015.146",
language = "English",
series = "Proceedings - 2015 International Conference on Computational Science and Computational Intelligence, CSCI 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "83--88",
editor = "Quoc-Nam Tran and Leonidas Deligiannidis and Arabnia, {Hamid R.}",
booktitle = "Proceedings - 2015 International Conference on Computational Science and Computational Intelligence, CSCI 2015",
}