PMF Model for Mining User Relations

Advances in sentiment analysis have enabled extraction of user relations implied in online textual exchanges such as forum posts. However, recent studies in this direction only consider direct relation extraction from text. As user interactions can be sparse in online discussions, we propose to appl...

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Main Authors: QIU, Minghui, YANG, Liu, JIANG, Jing
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Published: Institutional Knowledge at Singapore Management University 2013
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Online Access:https://ink.library.smu.edu.sg/researchdata/10
https://github.com/yangliuy/NLPForumPostOTE
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Institution: Singapore Management University
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spelling sg-smu-ink.researchdata-10092015-07-15T07:52:54Z PMF Model for Mining User Relations QIU, Minghui YANG, Liu JIANG, Jing Advances in sentiment analysis have enabled extraction of user relations implied in online textual exchanges such as forum posts. However, recent studies in this direction only consider direct relation extraction from text. As user interactions can be sparse in online discussions, we propose to apply collaborative filtering through probabilistic matrix factorization to generalize and improve the opinion matrices extracted from forum posts. This package implements the construction of opinion matrices which are the input of PMF model. The main features include aspect identification, opinion expression identification and opinion relation extraction based on dependency path rules. More details of our methods for aspect identification, opinion identification and opinion relation extraction are described in the related paper http://aclweb.org/anthology/N13-1041. 2013-01-01T08:00:00Z text application/x-7z-compressed https://ink.library.smu.edu.sg/researchdata/10 https://github.com/yangliuy/NLPForumPostOTE SMU Research Data Institutional Knowledge at Singapore Management University Computer Sciences
institution Singapore Management University
building SMU Libraries
country Singapore
collection InK@SMU
topic Computer Sciences
spellingShingle Computer Sciences
QIU, Minghui
YANG, Liu
JIANG, Jing
PMF Model for Mining User Relations
description Advances in sentiment analysis have enabled extraction of user relations implied in online textual exchanges such as forum posts. However, recent studies in this direction only consider direct relation extraction from text. As user interactions can be sparse in online discussions, we propose to apply collaborative filtering through probabilistic matrix factorization to generalize and improve the opinion matrices extracted from forum posts. This package implements the construction of opinion matrices which are the input of PMF model. The main features include aspect identification, opinion expression identification and opinion relation extraction based on dependency path rules. More details of our methods for aspect identification, opinion identification and opinion relation extraction are described in the related paper http://aclweb.org/anthology/N13-1041.
format text
author QIU, Minghui
YANG, Liu
JIANG, Jing
author_facet QIU, Minghui
YANG, Liu
JIANG, Jing
author_sort QIU, Minghui
title PMF Model for Mining User Relations
title_short PMF Model for Mining User Relations
title_full PMF Model for Mining User Relations
title_fullStr PMF Model for Mining User Relations
title_full_unstemmed PMF Model for Mining User Relations
title_sort pmf model for mining user relations
publisher Institutional Knowledge at Singapore Management University
publishDate 2013
url https://ink.library.smu.edu.sg/researchdata/10
https://github.com/yangliuy/NLPForumPostOTE
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