Language-independent sentiment analysis using subjectivity and positional information

Veselin Raychev, Preslav Nakov

Research output: Contribution to journalConference article

13 Citations (Scopus)

Abstract

We describe a novel language-independent approach to the task of determining the polarity, positive or negative, of the author's opinion on a specific topic in natural language text. In particular, weights are assigned to attributes, individual words or word bi-grams, based on their position and on their likelihood of being subjective. The subjectivity of each attribute is estimated in a two-step process, where first the probability of being subjective is calculated for each sentence containing the attribute, and then these probabilities are used to alter the attribute's weights for polarity classification. The evaluation results on a standard dataset of movie reviews shows 89.85% classification accuracy, which rivals the best previously published results for this dataset for systems that use no additional linguistic information nor external resources.

Original languageEnglish
Pages (from-to)360-364
Number of pages5
JournalInternational Conference Recent Advances in Natural Language Processing, RANLP
Publication statusPublished - 1 Dec 2009
EventInternational Conference on Recent Advances in Natural Language Processing, RANLP-2009 - Borovets, Bulgaria
Duration: 14 Sep 200916 Sep 2009

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Keywords

  • Polarity classification
  • Sentiment analysis
  • Subjectivity identification
  • Text categorization

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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