2026-09-18T19:32:25Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/405342022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Regina Barzilay.
Snyder, Benjamin, Ph. D. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2008-02-27T22:44:30Z
2008-02-27T22:44:30Z
2007
2007
http://hdl.handle.net/1721.1/40534
191958339
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 71-74).
We address the problem of analyzing multiple related opinions in a text. For instance, in a restaurant review such opinions may include food, ambiance and service. We formulate this task as a multiple aspect ranking problem, where the goal is to produce a set of numerical scores, one for each aspect. We present an algorithm that jointly learns ranking models for individual aspects by modeling the dependencies between assigned ranks. This algorithm guides the prediction of individual rankers by analyzing meta-relations between opinions, such as agreement and contrast. We provide an online training algorithm for our joint model which trains the individual rankers to operate in our framework. We prove that our agreement-based joint model is more expressive than individual ranking models, yet our training algorithm preserves the convergence guarantees of perceptron rankers. Our empirical results further confirm the strength of the model: the algorithm provides significant improvement over both individual rankers, a state-of-the-art joint ranking model, and ad-hoc methods for incorporating agreement.
by Benjamin Snyder.
S.M.
74 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Multiple aspect ranking for opinion analysis
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="3bb206d5-e208-4609-b41a-750b9043a034">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Multiple aspect ranking for opinion analysis</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2007</PublicationDate>
<Authors>
<Author>
<DisplayName>Snyder, Benjamin, Ph. D. Massachusetts Institute of Technology</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>We address the problem of analyzing multiple related opinions in a text. For instance, in a restaurant review such opinions may include food, ambiance and service. We formulate this task as a multiple aspect ranking problem, where the goal is to produce a set of numerical scores, one for each aspect. We present an algorithm that jointly learns ranking models for individual aspects by modeling the dependencies between assigned ranks. This algorithm guides the prediction of individual rankers by analyzing meta-relations between opinions, such as agreement and contrast. We provide an online training algorithm for our joint model which trains the individual rankers to operate in our framework. We prove that our agreement-based joint model is more expressive than individual ranking models, yet our training algorithm preserves the convergence guarantees of perceptron rankers. Our empirical results further confirm the strength of the model: the algorithm provides significant improvement over both individual rankers, a state-of-the-art joint ranking model, and ad-hoc methods for incorporating agreement.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>