Distributional semantic models for affective text analysis

Nikolaos Malandrakis, Alexandros Potamianos, Elias Iosif, Shrikanth Narayanan

Research output: Contribution to journalArticlepeer-review

Abstract

We present an affective text analysis model that can directly estimate and combine affective ratings of multi-word terms, with application to the problem of sentence polarity/semantic orientation detection. Starting from a hierarchical compositional method for generating sentence ratings, we expand the model by adding multi-word terms that can capture non-compositional semantics. The method operates similarly to a bigram language model, using bigram terms or backing off to unigrams based on a (degree of) compositionality criterion. The affective ratings for {\rm n}-gram terms of different orders are estimated via a corpus-based method using distributional semantic similarity metrics between unseen words and a set of seed words. {\rm N}-gram ratings are then combined into sentence ratings via simple algebraic formulas. The proposed framework produces state-of-the-art results for word-level tasks in English and German and the sentence-level news headlines classification SemEval'07-Task14 task. The inclusion of bigram terms to the model provides significant performance improvement, even if no term selection is applied.

Original languageEnglish
Article number6578101
Pages (from-to)2379-2392
Number of pages14
JournalIEEE Transactions on Audio, Speech and Language Processing
Volume21
Issue number11
DOIs
Publication statusPublished - 2013
Externally publishedYes

Keywords

  • Affect
  • affective lexicon
  • distributional semantic models
  • emotion
  • lexical semantics
  • natural language understanding
  • opinion mining
  • polarity detection
  • sentiment analysis
  • valence

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