USNA: A Dual-Classifier Approach to Contextual Sentiment Analysis

Abstract

This paper describes a dual-classifier approach to contextual sentiment analysis at the SemEval-2013 Task 2. Contextual analysis of polarity focuses on a word or phrase, rather than the broader task of identifying the sentiment of an entire text. The Task 2 definition includes target word spans that range in size from a single word to entire sentences. However the context of a single word is dependent on the word's surrounding syntax, while a phrase contains most of the polarity within itself. We thus describe separate treatment with two independent classifiers, outperforming the accuracy of a single classifier. Our system ranked 6th out of 19 teams on SMS message classification, and 8th of 23 on twitter data. We also show a surprising result that a very small amount of word context is needed for high-performance polarity extraction.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2013
Accession Number
ADA587816

Entities

People

  • Eugene Yang
  • Ganesh Harihara
  • Nathanael Chambers

Organizations

  • United States Naval Academy

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Human Systems

DTIC Thesaurus Topics

  • Accuracy
  • Classification
  • Computational Linguistics
  • Computer Science
  • Data Mining
  • Information Science
  • Language
  • Linguistics
  • Machine Learning
  • Natural Language Processing
  • Online Communications
  • Polarity
  • Social Media
  • Social Networking Services
  • Text Messaging
  • Training
  • United States Naval Academy

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Speech Processing/Speech Recognition.
  • Systems Analysis and Design