Design and validation of annotation schemas for aspect-based sentiment analysis in the tourism sector

Abstract

The use of linguistic resources beyond the scope of language studies, e.g., commercial purposes, has become commonplace since the availability of massive amounts of data and the development of software tools to process them. An interesting perspective on these data is provided by Sentiment Analysis, which attempts to identify the polarity of a text, but can also pursue further, more challenging aims, such as the automatic identification of the specific entities and aspects being discussed in the evaluative speech act, along with the polarity associated with them. This approach, known as aspect-based sentiment analysis, seeks to offer fine-grained information from raw text, but its success depends largely on the existence of pre-annotated domain-specific corpora, which in turn calls for the design and validation of an annotation schema. This paper examines the methodological aspects involved in the creation of such annotation schema and is motivated by the scarcity of information found in the literature. We describe the insights we obtained from the annotation schema generation and validation process within our project, whose objectives include the development of advanced sentiment analysis software of user reviews in the tourism sector. We focus on the identification of the relevant entities and attributes in the domain, which we extract from a corpus of user reviews, and go on to describe the schema creation and validation process. We begin by describing the corpus annotation process and its further iterative refinement by means of several inter-annotator agreement measurements, which we believe is key to a successful annotation schema.

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Notes

  1. 1.

    As the nomenclature followed for aspect-based sentiment analysis might be sometimes a bit confusing, for this work we decided to follow Pontiki et al. (2016) premises. Thus, an entity is considered the object of evaluation defined by an “is–a” relationship, whereas attribute (or aspect) refers to features characterizing an entity according to a “has–a” association.

  2. 2.

    A complete survey of the approaches employed in ABSA can be found in Schouten and Frasincar (2016).

  3. 3.

    The SentiTur project is a continuation of Lingmotif2. The proposal, submitted to the regional government of Andalusia under the European Regional Development Fund (ERDF) funding program, is currently under evaluation. The project’s main objective is the creation of an online system that displays detailed information on users’ opinions of Andalusian tourism resources, employing the ABSA methodology described here as well as an advanced visualization system based on Visual Analytics (Kohlhammer et al. 2011) and Formal Component Analysis (Ganter and Wille 1999).

  4. 4.

    Data obtained from the official website of the Andalusian Government https://www.juntadeandalucia.es/andalucia/economia/turismo.html.

  5. 5.

    https://scrapy.org.

  6. 6.

    The software used was modified from the one available at https://github.com/monkeylearn/hotel-review-analysis.

  7. 7.

    Available at http://brat.nlplab.org.

  8. 8.

    Annotators are also mentioned by the synonyms raters and coders.

  9. 9.

    http://www.nltk.org/api/nltk.metrics.html#module-nltk.metrics.agreement.

  10. 10.

    The t-test shows a significant difference between Trial 1 and 2, p < 0.01.

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Funding

This research has been sponsored by the Spanish Government under Grant FFI2016-78141-P (Lingmotif2).

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Correspondence to Soluna Salles-Bernal.

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Moreno-Ortiz, A., Salles-Bernal, S. & Orrequia-Barea, A. Design and validation of annotation schemas for aspect-based sentiment analysis in the tourism sector. Inf Technol Tourism 21, 535–557 (2019). https://doi.org/10.1007/s40558-019-00155-0

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Keywords

  • Annotation schema
  • Aspect-based sentiment analysis
  • Inter-rater agreement
  • Tourism industry
  • User-generated content