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The FeatureSent 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))

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Abstract

The approach described in this paper explores the use of semantic structured representation of sentences extracted from texts for multi-domain sentiment analysis purposes. The presented algorithm is built upon a domain-based supervised approach using index-like structured for representing information extracted from text. The algorithm extracts dependency parse relationships from the sentences containing in a training set. Then, such relationships are aggregated in a semantic structured together with either polarity and domain information. Such information is exploited in order to have a more fine-grained representation of the learned sentiment information. When the polarity of a new text has to be computed, such a text is converted in the same semantic representation that is used (i) for detecting the domain to which the text belongs to, and then (ii), once the domain is assigned to the text, the polarity is extracted from the index-like structure. First experiments performed by using the Blitzer dataset for training the system demonstrated the feasibility of the proposed approach.

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Notes

  1. 1.

    The list of stop words used in this work is the one provided by Apache with the Lucene and Solr packages.

  2. 2.

    The test set is available at https://goo.gl/siOJbZ.

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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 FeatureSent 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_17

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