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DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing

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Abstract

In recent years, SenticNet and OntoSenticNet have represented important developments in the novel interdisciplinary field of research known as sentic computing, enabling the development of a variety of Sentic applications. In this paper, we propose an extension of the OntoSenticNet ontology, named DomainSenticNet, and contribute an unsupervised methodology to support the development of domain-aware Sentic applications. We developed an unsupervised methodology that, for each concept in OntoSenticNet, mines semantically related concepts from WordNet and Probase knowledge bases and computes domain distributional information from the entire collection of Kickstarter domain-specific crowdfunding campaigns. Subsequently, we applied DomainSenticNet to a prototype tool for Kickstarter campaign authoring and success prediction, demonstrating an improvement in the interpretability of sentiment intensities. DomainSenticNet is an extension of the OntoSenticNet ontology that integrates each of the 100,000 concepts included in OntoSenticNet with a set of semantically related concepts and domain distributional information. The defined unsupervised methodology is highly replicable and can be easily adapted to build similar domain-aware resources from different domain corpora and external knowledge bases. Used in combination with OntoSenticNet, DomainSenticNet may favor the development of novel hybrid aspect-based sentiment analysis systems and support further research on sentic computing in domain-aware applications.

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Notes

  1. Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). https://en.wikipedia.org/wiki/Coronavirus_disease_2019

  2. https://www.kickstarter.com/

  3. https://creativecommons.org/licenses/by/4.0/deed.en

  4. Monthly updated dataset of the Kickstarter campaign URLs is available at: https://webrobots.io/kickstarter-datasets/

  5. Real-time statistics are accessible at: https://www.kickstarter.com/help/stats

  6. We were able to crawl a total of \(\sim\)230K Kickstarter descriptions from the original \(\sim\)480K campaigns.

  7. An overview of the respective domains and related statistics is available at: https://www.kickstarter.com/help/stats

  8. Real-time data are widely recognized as the life blood of a variety of applications (e.g., [10])

  9. https://webrobots.io/kickstarter-datasets/

  10. https://github.com/needindex/domainsenticnet

  11. https://github.com/needindex/gameon

  12. It is worth noting that the tool can also process the human-crafted partitions of the domain aspects.

  13. OKR models are commonly used by very successful companies such as Amazon, Facebook, and Google. https://www.whatmatters.com/faqs/how-to-grade-okrshttps://conceptboard.com/blog/okr-google-goal-setting-success/

  14. The education cluster groups the following aspects: “education,” “student,” “school,” “college,” “instruction,” “classroom,” “brain,” “growth,” “level,” “course,” “knowledge,” “career,” “tutorial,” “education,” “lecture,” “tutor,” “teacher,” “learning,” “teaching,” and “skil.l”

  15. SenticNet 6 has recently been released. This updated resource now contains 200K concepts [8]

  16. A recent model revision is described in [35]

  17. https://microsoft.github.io/dowhy/dowhy_confounder_example.html

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Acknowledgements

The work of Paolo Rosso was partially funded by the Spanish MICINN under the project PGC2018-096212-B-C31.

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Authors and Affiliations

  1. University of Rome Unitelma Sapienza, Rome, Italy

    Damiano Distante & Stefano Faralli

  2. Independent researcher, Freelancer Digital Transformation, Baden-Wurttemberg, Germany

    Steve Rittinghaus

  3. Universitat Politcnica de Valncia, Valencia, Spain

    Paolo Rosso

  4. Independent researcher, Software Architect, Baden-Wurttemberg, Germany

    Nima Samsami

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Correspondence to Stefano Faralli.

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Distante, D., Faralli, S., Rittinghaus, S. et al. DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing . Cogn Comput 14, 62–77 (2022). https://doi.org/10.1007/s12559-021-09825-w

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