Sentiment Classification via a Response Recalibration Framework

Phillip Smith, M. Lee

Research output: Contribution to conferencePaper

17 Downloads (Pure)

Abstract

Probabilistic learning models have the ability to be calibrated to improve the performance of tasks such as sentiment classification. In this paper, we introduce a framework for sentiment classification that enables classifier recalibration given the presence of related, context-bearing documents. We investigate the use of probabilistic thresholding and document similarity based recalibration methods to yield classifier improvements. We demonstrate the performance of our proposed recalibration methods on a dataset of online clinical reviews from the patient feedback domain that have adjoining management responses that yield sentiment bearing information. Experimental results show the proposed recalibration methods outperform uncalibrated supervised machine learning models trained for sentiment analysis, and yield significant improvements over a robust baseline.
Original languageEnglish
Pages175-180
Publication statusPublished - 2015
EventWorkshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis - Lisboa, Portugal
Duration: 17 Sep 201517 Sep 2015

Workshop

WorkshopWorkshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
CountryPortugal
CityLisboa
Period17/09/1517/09/15

Bibliographical note

The full text is available from: http://www.emnlp2015.org/proceedings/WASSA/WASSA-2015.pdf

Fingerprint Dive into the research topics of 'Sentiment Classification via a Response Recalibration Framework'. Together they form a unique fingerprint.

  • Cite this

    Smith, P., & Lee, M. (2015). Sentiment Classification via a Response Recalibration Framework. 175-180. Paper presented at Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, Lisboa, Portugal.