Multiple aspect ranking for opinion analysis
Name
191958339-MIT.pdf
Description
Full printable version
Size
2.8 MB
Format
Adobe PDF
Checksum (MD5)
efeead778d11432933ef1decd8a6dd7e
Author(s)
Snyder, Benjamin, Ph. D. Massachusetts Institute of Technology
Advisor(s)
Regina Barzilay.
Date Issued
2007
Publisher
Massachusetts Institute of Technology
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.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 71-74).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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.
Persistent DSpace Link
