Wei Xue

FIU SCIS


Lecture Information:
  • December 4, 2017
  • 1:30 PM
  • ECS-349

Speaker Bio

Wei Xue received his B.S. and M.S. degrees in computer science from Zhejiang University, China in 2008 and 2011, respectively. He is currently a PhD. candidate in the School of Computing and Information Sciences at the Florida International University (FIU), Knowledge Discovery Research Group (KDRG), led by Dr. Tao Li. His research interest involves natural language processing and text mining.

Description

With proliferation of user-generated reviews, new opportunities and challenges arise. The advance of Web technologies allows people easily 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 the vast pool of reviews enable 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 over the aspects of the reviewed entities.

In this dissertation, we study 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 focus on 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 propose a few probabilistic graphical models, which can model words distribution in reviews and aspect ratings at the same time. Second, we propose 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 propose a gated convolution neural network, which can be applied to aspect category sentiment analysis as well as aspect target sentiment analysis.