Skip to main content
×
Home
    • Aa
    • Aa

Subjectivity detection in spoken and written conversations

  • GABRIEL MURRAY (a1) and GIUSEPPE CARENINI (a1)
Abstract
Abstract

In this work we investigate four subjectivity and polarity tasks on spoken and written conversations. We implement and compare several pattern-based subjectivity detection approaches, including a novel technique wherein subjective patterns are learned from both labeled and unlabeled data, using n-gram word sequences with varying levels of lexical instantiation. We compare the use of these learned patterns with an alternative approach of using a very large set of raw pattern features. We also investigate how these pattern-based approaches can be supplemented and improved with features relating to conversation structure. Experimenting with meeting speech and email threads, we find that our novel systems incorporating varying instantiation patterns and conversation features outperform state-of-the-art systems despite having no recourse to domain-specific features such as prosodic cues and email headers. In some cases, such as when working with noisy speech recognizer output, a small set of well-motivated conversation features performs as well as a very large set of raw patterns.

Copyright
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

N. Baron 2000. Alphabet to Email: How Written English Evolved and Where it's Heading. New York, NY: Routledge (Taylor & Francis).

E. Brill 1992. A simple rule-based part of speech tagger. In Proceedings of Darpa Speech and Natural Language Workshop, San Mateo, CA, pp. 112116.

G. Carenini , R. Ng , and X. Zhou 2007. Summarizing email conversations with clue words. In Proceedings of Acm www 07, Banff, Canada.

T. Wilson , J. Wiebe , and R. Hwa 2006. Recognizing strong and weak opinion clauses. Computational Intelligence 22 (2): 7399.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Natural Language Engineering
  • ISSN: 1351-3249
  • EISSN: 1469-8110
  • URL: /core/journals/natural-language-engineering
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×