JSM 2015 Preliminary Program

Online Program Home
My Program

Abstract Details

Activity Number: 47
Type: Invited
Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
Sponsor: IMS
Abstract #314371 View Presentation
Title: Classification with Unstructured Predictors with an Application to Sentiment Analysis
Author(s): Annie Qu* and Xiaotong Shen and Junhui Wang and Yiwen Sun
Companies: University of Illinois at Urbana-Champaign and University of Minnesota and University of Illinois at Chicago and University of Minnesota
Keywords: Natural language processing ; Large margin learners ; Large $n$ and $p$ ; Sentiment analysis ; Text and opinion mining ; Unstructured data
Abstract:

This talk explores ordinal classification for unstructured predictors with ordered class categories, where imprecise information concerning strengths between predictors is available for predicting class labels. However, imprecise information here is expressed in terms of a directed graph, with each node representing a predictor and a directed edge containing pairwise strengths between two nodes. One of the targeted applications for unstructured data arises from sentiment analysis, which identifies and extracts the relevant content or opinion of a document concerning a specific event of interest. We integrate the imprecise predictor relations into linear relational constraints over classification function coefficients, where large margin ordinal classifiers are introduced, subject to many quadratically linear constraints. We implement ordinal support vector machines and $\psi$-learning through a scalable quadratic programming package based on sparse word representations. Theoretically, we also show that utilizing relationships among unstructured predictors improves prediction accuracy of classification significantly.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2015 program





For program information, contact the JSM Registration Department or phone (888) 231-3473.

For Professional Development information, contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

2015 JSM Online Program Home