Document Type
Dissertation
Degree
Doctor of Philosophy (PhD)
Major/Program
Computer Science
First Advisor's Name
Tao Li
First Advisor's Committee Title
Committee Chair
Second Advisor's Name
Jaime Leonardo Bobadilla
Second Advisor's Committee Title
Committee Member
Third Advisor's Name
Ning Xie
Third Advisor's Committee Title
Committee Member
Fourth Advisor's Name
Debra VanderMeer
Fourth Advisor's Committee Title
Committee Member
Fifth Advisor's Name
Sundaraja Sitharama Iyengar
Fifth Advisor's Committee Title
Committee Member
Keywords
sentiment analysis, text mining
Date of Defense
12-4-2017
Abstract
With proliferation of user-generated reviews, new opportunities and challenges arise. The advance of Web technologies allows people to access a large amount of reviews of products and services online. Knowing what others like and dislike becomes increasingly important for their decision making in online shopping. The retailers also care more than ever about online reviews, because a vast pool of reviews enables them to monitor reputations and collect feedbacks efficiently. However, people often find difficult times in identifying and summarizing fine-grained sentiments buried in the opinion-rich resources. The traditional sentiment analysis, which focuses on the overall sentiments, fails to uncover the sentiments with regard to the aspects of the reviewed entities.
This dissertation studied the research problem of Aspect Based Sentiment Analysis (ABSA), which is to reveal the aspect-dependent sentiment information of review text. ABSA consists of several subtasks: 1) aspect extraction, 2) aspect term extraction, 3) aspect category classification, and 4) sentiment polarity classification at aspect level. We focused on the approach of topic models and neural networks for ABSA. First, to extract the aspects from a collection of reviews and to detect the sentiment polarity regarding the aspects in each review, we proposed a few probabilistic graphical models, which can model words distribution in reviews and aspect ratings at the same time. Second, we presented a multi-task learning model based on long-short term memory and convolutional neural network for aspect category classification and aspect term extraction. Third, for aspect-level sentiment polarity classification, we developed a gated convolution neural network, which can be applied to aspect category sentiment analysis as well as aspect target sentiment analysis.
Identifier
FIDC004076
ORCID
https://orcid.org/0000-0003-1341-2189
Recommended Citation
Xue, Wei, "Aspect Based Sentiment Analysis On Review Data" (2017). FIU Electronic Theses and Dissertations. 3721.
https://digitalcommons.fiu.edu/etd/3721
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