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The good, the bad and the implicit: a comprehensive approach to annotating explicit and implicit sentiment

Abstract

We present a fine-grained scheme for the annotation of polar sentiment in text, that accounts for explicit sentiment (so-called private states), as well as implicit expressions of sentiment (polar facts). Polar expressions are annotated below sentence level and classified according to their subjectivity status. Additionally, they are linked to one or more targets with a specific polar orientation and intensity. Other components of the annotation scheme include source attribution and the identification and classification of expressions that modify polarity. In previous research, little attention has been given to implicit sentiment, which represents a substantial amount of the polar expressions encountered in our data. An English and Dutch corpus of financial newswire text, consisting of over 45,000 words each, was annotated using our scheme. A subset of this corpus was used to conduct an inter-annotator agreement study, which demonstrated that the proposed scheme can be used to reliably annotate explicit and implicit sentiment in real-world textual data, making the created corpora a useful resource for sentiment analysis.

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Notes

  1. http://mpqa.cs.pitt.edu/corpora/mpqa_corpus/

  2. Wilson (2008) states that an utterance “may be a single phrase or expression, but whenever possible it is a sentence or proposition with references to the source and target of the subjectivity included in the span that is marked”.

  3. When applying our fine-grained annotation scheme to text, we make use of the brat annotation tool (see Sect. 4.2). In brat, the polarity of the sentiment expressed by a polar expression about a certain target is denoted by the colour of the arrow pointing from the polar expression to that target (viz. green for positive, red for negative, purple for unknown and orange for other) and a symbol (viz. +, −, ? and \(\sim \)).

  4. Other sources use the terms objective polar utterances (Wilson 2008) or evaluative factuals (Nigam and Hurst 2004) for polar facts.

  5. The same applies to the other elements covered by our annotation scheme, viz. modifiers, sources, source expressions, targets and causes.

  6. These modifiers are also sometimes referred to as amplifiers (Quirk et al. 1985).

  7. These modifiers are also sometimes referred to as downplayers or downtoners (Quirk et al. 1985).

  8. In brat, the intensity of the positive or negative sentiment is denoted by the number of plus or minus signs accompanying the arrow pointing from the polar expression to the target at hand.

  9. While Girju (2003) uses the term resultative causative for verbal constructions, we also identify other causative constructions.

  10. The resulting annotations will be made available for research purposes.

  11. Note that not all annotators for Dutch (three Dutch native speakers) are the same persons who annotated the English corpus (two Master’s students in English and one English native speaker). Only one annotator participated in the inter-annotator agreement study for both English and Dutch.

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Acknowledgments

This research was conducted in the framework of SentiFM (http://www.lt3.ugent.be/en/projects/sentifm/) (Sentiment mining for Financial Markets), a project funded by a Ph.D. grant of the Agency for Innovation by Science and Technology (IWT), and the HOF project SubTLe (http://www.lt3.ugent.be/en/projects/subtle/) (Subjectivity Tagging and Learning).

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Van de Kauter, M., Desmet, B. & Hoste, V. The good, the bad and the implicit: a comprehensive approach to annotating explicit and implicit sentiment. Lang Resources & Evaluation 49, 685–720 (2015). https://doi.org/10.1007/s10579-015-9297-4

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Keywords

  • Corpus annotation
  • Polarity
  • Sentiment analysis
  • Natural language processing