IDEAS home Printed from https://ideas.repec.org/a/ids/ijbisy/v32y2019i1p30-55.html
   My bibliography  Save this article

Ontology-based feature-level sentiment analysis in Portuguese reviews

Author

Listed:
  • Larissa Astrogildo De Freitas
  • Renata Vieira

Abstract

Sentiment analysis is the field of study that analyses people's opinions in texts. In the last decade, humans have come to share their opinions in social media on the web. Opinions are important because whenever we need to take a decision, we want to know others' points of view. In this work we study this more detailed level of analysis, which is called the aspect-level. This more detailed type of analysis requires going deeper in the parts of a sentence and an effort to deal with their meaning, therefore knowledge rich approaches are useful. For that we use ontologies, as they can be used to represent the relevant aspects of entities, in a semantic organized way, for instance, aspects that are 'part-of' or properties of an entity in a domain of knowledge. Our approach is built for Portuguese and is evaluated on a reviews data set of the accommodation sector.

Suggested Citation

  • Larissa Astrogildo De Freitas & Renata Vieira, 2019. "Ontology-based feature-level sentiment analysis in Portuguese reviews," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 32(1), pages 30-55.
    • Handle: RePEc:ids:ijbisy:v:32:y:2019:i:1:p:30-55
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=102698
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijbisy:v:32:y:2019:i:1:p:30-55. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=172 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.