Matches in ESWC 2020 for { ?s ?p The paper discusses a semi-automatic ontology builder approach for addressing the aspect-based sentiment analysis task. The paper is well written but the novelty is not completely clear since the number of approaches proposed in the literature about this topic is definitely huge. Anyway, I invite the authors to fix this work in two directions: related work and experiments. The related work section should include more recent contribution concerning the use of learning and argumentation techniques for aspect-based sentiment analysis. For example: - Mauro Dragoni, Célia da Costa Pereira, Andrea G. B. Tettamanzi, Serena Villata: Combining argumentation and aspect-based opinion mining: The SMACk system. AI Commun. 31(1): 75-95 (2018) Moreover, a discussion comparing the proposed contribution with respect to the papers cited within the related work section should be properly included where missing or expanded. The authors are invited to include in the related work section also works adopting semantic techniques for detecting aspects and opinions. An example is the following paper: - Marco Federici, Mauro Dragoni: A Knowledge-Based Approach for Aspect-Based Opinion Mining. SemWebEval@ESWC 2016: 141-152 While, concerning the evaluation, the authors are invited to test the algorithms also on the SemEval 2015 aspect-based benchmark. Moreover, results obtained by other SemEval campaign participants should be reported and discussed. ------------------ I thank the author for the effort in preparing the rebuttal. I confirm my score and I hope to see this paper accepted for ESWC.". }
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- Paper.28_Review.1 hasContent "The paper discusses a semi-automatic ontology builder approach for addressing the aspect-based sentiment analysis task. The paper is well written but the novelty is not completely clear since the number of approaches proposed in the literature about this topic is definitely huge. Anyway, I invite the authors to fix this work in two directions: related work and experiments. The related work section should include more recent contribution concerning the use of learning and argumentation techniques for aspect-based sentiment analysis. For example: - Mauro Dragoni, Célia da Costa Pereira, Andrea G. B. Tettamanzi, Serena Villata: Combining argumentation and aspect-based opinion mining: The SMACk system. AI Commun. 31(1): 75-95 (2018) Moreover, a discussion comparing the proposed contribution with respect to the papers cited within the related work section should be properly included where missing or expanded. The authors are invited to include in the related work section also works adopting semantic techniques for detecting aspects and opinions. An example is the following paper: - Marco Federici, Mauro Dragoni: A Knowledge-Based Approach for Aspect-Based Opinion Mining. SemWebEval@ESWC 2016: 141-152 While, concerning the evaluation, the authors are invited to test the algorithms also on the SemEval 2015 aspect-based benchmark. Moreover, results obtained by other SemEval campaign participants should be reported and discussed. ------------------ I thank the author for the effort in preparing the rebuttal. I confirm my score and I hope to see this paper accepted for ESWC."".