Wei XueFollow

Document Type



Doctor of Philosophy (PhD)


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


sentiment analysis, text mining

Date of Defense



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.






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