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Semantic Web Evaluation Challenge

SemWebEval 2018: Semantic Web Challenges pp 197–215Cite as

The NeuroSent System at ESWC-2018 Challenge on Semantic Sentiment Analysis

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 927))

Abstract

Multi-domain sentiment analysis consists in estimating the polarity of a given text by exploiting domain-specific information. One of the main issues common to the approaches discussed in the literature is their poor capabilities of being applied on domains which are different from those used for building the opinion model. In this paper, we will present an approach exploiting the linguistic overlap between domains to build sentiment models supporting polarity inference for documents belonging to every domain. Word embeddings together with a deep learning architecture have been implemented for enabling the building of multi-domain sentiment model. The proposed technique is validated by following the Dranziera protocol in order to ease the repeatability of the experiments and the comparison of the results. The outcomes demonstrate the effectiveness of the proposed approach and also set a plausible starting point for future work.

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Notes

  1. 1.

    https://deeplearning4j.org/.

  2. 2.

    https://github.com/mmihaltz/word2vec-GoogleNews-vectors.

  3. 3.

    https://github.com/mmihaltz/word2vec-GoogleNews-vectors.

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Authors and Affiliations

  1. Fondazione Bruno Kessler, Trento, Italy

    Mauro Dragoni

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  1. Mauro Dragoni
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  1. Laboratoire d'Informatique de Paris-Nord, Paris 13 University, Villetaneuse, France

    Davide Buscaldi

  2. University of Bologna, Bologna, Italy

    Aldo Gangemi

  3. Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy

    Diego Reforgiato Recupero

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Dragoni, M. (2018). The NeuroSent System at ESWC-2018 Challenge on Semantic Sentiment Analysis. In: Buscaldi, D., Gangemi, A., Reforgiato Recupero, D. (eds) Semantic Web Challenges. SemWebEval 2018. Communications in Computer and Information Science, vol 927. Springer, Cham. https://doi.org/10.1007/978-3-030-00072-1_16

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