Main content

A platform upgrade will be performed on the Atrium Institutional Repository from Monday, July 13 to Wenesday, July 15, 2020 (inclusive). During this time, users will not be able to submit new items to the Atrium. Users will still be able to browse, view, and download items that are already available in the Atrium. We apologize for any inconvenience this may cause.

A Unified Probabilistic Model for Aspect-Level Sentiment Analysis

Show full item record

Title: A Unified Probabilistic Model for Aspect-Level Sentiment Analysis
Author: Stantic, Daniel
Department: School of Computer Science
Program: Computer Science
Advisor: Song, Fei
Abstract: In this thesis, we develop a new probabilistic model for aspect-level sentiment analysis based on POSLDA, a topic classifier that incorporates syntax modelling for better performance. POSLDA separates semantic words from purely functional words and restricts its topic modelling on the semantic words. We take this a step further by modelling the probability of a semantic word expressing sentiment based on its part-of-speech class and then modelling its sentiment if it is a sentiment word. We restructure the popular approach of topic-sentiment distributions within documents and add a few novel heuristic improvements. Our experiments demonstrate that our model produces results competitive to the state of the art systems. In addition to the model, we develop a multi-threaded version of the popular Gibbs sampling algorithm that can perform inference over 1000 times faster than the traditional implementation while preserving the quality of the results.
Date: 2016-04
Rights: Attribution-NoDerivs 2.5 Canada
Terms of Use: All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.

Files in this item

Files Size Format View Description
Stantic_Daniel_201604_MSc.pdf 1.188Mb PDF View/Open Thesis

This item appears in the following Collection(s)

Show full item record

Attribution-NoDerivs 2.5 Canada Except where otherwise noted, this item's license is described as Attribution-NoDerivs 2.5 Canada