In this paper we present a novel framework for extracting the ratable aspects
of objects from online user reviews. Extracting such aspects is an important
challenge in automatically mining product opinions from the web and in
generating opinion-based summaries of user reviews. Our models are based on
extensions to standard topic modeling methods such as LDA and PLSA to induce
multi-grain topics. We argue that multi-grain models are more appropriate for
our task since standard models tend to produce topics that correspond to global
properties of objects (e.g., the brand of a product type) rather than the
aspects of an object that tend to be rated by a user. The models we present not
only extract ratable aspects, but also cluster them into coherent topics, e.g.,
`waitress' and `bartender' are part of the same topic `staff' for restaurants.
This differentiates it from much of the previous work which extracts aspects
through term frequency analysis with minimal clustering. We evaluate the
multi-grain models both qualitatively and quantitatively to show that they
improve significantly upon standard topic models.
Description
Modeling Online Reviews with Multi-grain Topic Models
%0 Generic
%1 titov2008modeling
%A Titov, Ivan
%A McDonald, Ryan
%D 2008
%K lda model multi reviews source toread
%T Modeling Online Reviews with Multi-grain Topic Models
%U http://arxiv.org/abs/0801.1063
%X In this paper we present a novel framework for extracting the ratable aspects
of objects from online user reviews. Extracting such aspects is an important
challenge in automatically mining product opinions from the web and in
generating opinion-based summaries of user reviews. Our models are based on
extensions to standard topic modeling methods such as LDA and PLSA to induce
multi-grain topics. We argue that multi-grain models are more appropriate for
our task since standard models tend to produce topics that correspond to global
properties of objects (e.g., the brand of a product type) rather than the
aspects of an object that tend to be rated by a user. The models we present not
only extract ratable aspects, but also cluster them into coherent topics, e.g.,
`waitress' and `bartender' are part of the same topic `staff' for restaurants.
This differentiates it from much of the previous work which extracts aspects
through term frequency analysis with minimal clustering. We evaluate the
multi-grain models both qualitatively and quantitatively to show that they
improve significantly upon standard topic models.
@misc{titov2008modeling,
abstract = {In this paper we present a novel framework for extracting the ratable aspects
of objects from online user reviews. Extracting such aspects is an important
challenge in automatically mining product opinions from the web and in
generating opinion-based summaries of user reviews. Our models are based on
extensions to standard topic modeling methods such as LDA and PLSA to induce
multi-grain topics. We argue that multi-grain models are more appropriate for
our task since standard models tend to produce topics that correspond to global
properties of objects (e.g., the brand of a product type) rather than the
aspects of an object that tend to be rated by a user. The models we present not
only extract ratable aspects, but also cluster them into coherent topics, e.g.,
`waitress' and `bartender' are part of the same topic `staff' for restaurants.
This differentiates it from much of the previous work which extracts aspects
through term frequency analysis with minimal clustering. We evaluate the
multi-grain models both qualitatively and quantitatively to show that they
improve significantly upon standard topic models.},
added-at = {2013-03-25T20:48:26.000+0100},
author = {Titov, Ivan and McDonald, Ryan},
biburl = {https://www.bibsonomy.org/bibtex/2f3286f5efa0115f465563d0259c32255/hotho},
description = {Modeling Online Reviews with Multi-grain Topic Models},
interhash = {00cbf1df09c3f2c65d5a31a0537aed3f},
intrahash = {f3286f5efa0115f465563d0259c32255},
keywords = {lda model multi reviews source toread},
note = {cite arxiv:0801.1063},
timestamp = {2013-03-25T20:50:23.000+0100},
title = {Modeling Online Reviews with Multi-grain Topic Models},
url = {http://arxiv.org/abs/0801.1063},
year = 2008
}