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Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis

Summary

Sentiment lexicons list a word's out-of-context (prior) polarity, but an actual occurrence of that word may carry a different, or no, sentiment in its sentence. Wilson, Wiebe and Hoffmann study how to automatically recognise this contextual polarity at the phrase level. They added contextual polarity labels to the subjective expressions already marked in the MPQA opinion corpus and showed that annotators can apply these labels consistently. They then took a large prior-polarity lexicon and classified each clue instance found in the corpus in two stages. The first stage separates neutral from polar uses, and the second decides between positive and negative. Many linguistically motivated features were tested across several machine learning algorithms. The most accurate results usually came from combining all features. Negation features mattered most for the positive/negative decision. Neutral instances turned out to be the main obstacle. This makes the work a key reference for phrase-level opinion mining, opinion question answering and summarisation.

Key points

  • Resource: contextual polarity annotations were layered onto the existing Multi-perspective Question Answering (MPQA) corpus, and an agreement study confirmed the labels are reliable.
  • Frequency of neutral uses: lexicon words with positive or negative prior polarity occur in neutral contexts far more often than with flipped polarity.
  • Pipeline: a two-step classifier first labels clue instances as neutral or polar, then assigns positive or negative to the polar ones. Features cover negation (local and long-distance), modality, word sense, syntactic role, diminishers/intensifiers, document topic and interactions between clues.
  • Feature results: for neutral vs. polar, single features improve only recall or precision, and only the full combination significantly beats the baselines in accuracy. For positive vs. negative, negation features are strongest, followed by features capturing clues modifying each other.
  • Effect of neutral data: including neutral instances sharply weakens positive/negative features, so better neutral detection is identified as the most promising route to overall gains.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Cited by

102 papers of our library cite it (of the 140884 whose bibliographies we read); 8 of them have a page here, 94 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli.08-012-r1-06-90

@article{wilson2009recognizing,
  title = {{Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis}},
  author = {Wilson, Theresa and Wiebe, Janyce and Hoffmann, Paul},
  journal = {Computational Linguistics},
  year = {2009},
  doi = {10.1162/coli.08-012-r1-06-90},
  url = {https://doi.org/10.1162/coli.08-012-r1-06-90}
}

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