@inproceedings{9449fbe167a9479ca3039d26d64a980c,
title = "Shell Miner: Mining Organizational Phrases in Argumentative Texts in Social Media",
abstract = "Threaded debate forums have become one of the major social media platforms. Usually people argue with one another using not only claims and evidences about the topic under discussion but also language used to organize them, which we refer to as shell. In this paper, we study how to separate shell from topical contents using unsupervised methods. Along this line, we develop a latent variable model named Shell Topic Model (STM) to jointly model both topics and shell. Experiments on real online debate data show that our model can find both meaningful shell and topics. The results also show the effectiveness of our model by comparing it with several baselines in shell phrases extraction and document modeling.",
keywords = "argumentative text, latent variable model, organizational phrases, topic modeling",
author = "Jianguang Du and Jing Jiang and Liu Yang and Dandan Song and Lejian Liao",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 14th IEEE International Conference on Data Mining, ICDM 2014 ; Conference date: 14-12-2014 Through 17-12-2014",
year = "2014",
month = jan,
day = "1",
doi = "10.1109/ICDM.2014.98",
language = "English",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "January",
pages = "797--802",
editor = "Ravi Kumar and Hannu Toivonen and Jian Pei and \{Zhexue Huang\}, Joshua and Xindong Wu",
booktitle = "Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014",
address = "United States",
edition = "January",
}