A vector space approach for aspect-based sentiment analysis
Name
930610466-MIT.pdf
Description
Full printable version
Size
1.75 MB
Format
Adobe PDF
Checksum (MD5)
cabcd7489b65b1cc3d476b0bbca515f0
Author(s)
Alghunaim, Abdulaziz (Abdulaziz K.)
Advisor(s)
Mitra Mohtarami and James Glass.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
Vector representations for language have been shown to be useful in a number of Natural Language Processing (NLP) tasks. In this thesis, we aim to investigate the effectiveness of word vector representations for the research problem of Aspect-Based Sentiment Analysis (ABSA), which attempts to capture both semantic and sentiment information encoded in user generated content such as product reviews. In particular, we target three ABSA sub-tasks: aspect term extraction, aspect category detection, and aspect sentiment prediction. We investigate the effectiveness of vector representations over different text data, and evaluate the quality of domain-dependent vectors. We utilize vector representations to compute various vector-based features and conduct extensive experiments to demonstrate their eectiveness. Using simple vector-based features, we achieve F1 scores of 79.9% for aspect term extraction, 86.7% for category detection, and 72.3% for aspect sentiment prediction.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 75-82).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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