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Matches in ESWC 2020 for { ?s ?p The paper presents a contribution on Aspect-based Sentiment Analysis, that is, sentiment analysis where the sentence may contains more opinions on a number of aspects, with potentially different polarity. The work is based/compares against the previous work of one of the authors. The previous approach, based on a given ontology exploited to improve the sentiment analysis task, is improved in this work through semi-automatic generation of the ontology based on external resources providing synsets. This work almost reaches the performance of the previous approach, but require less time to the user to construct the ontology due to the semi-automatic generation. The paper needs some extra work to improve the presentation, and it is difficult to read in some points. In the following some examples: On page 3 "finally the resulting ontology...", which ontology is it? Further, you are describing data (section 3) without describing your approach first. Regarding SemEval-2016 data, authors mention that has "a target "word, aspect, sentiment and sentiment score", but a map to terms used in Fig 1 would be beneficial (e.g. where is the sentiment score in the provided example?) "The term extraction method that is used has score based on domain pertinence (DP) and domain consensus (DC)" Is this part of a new contribution or you are just using results in [12]? In general it is not clear since the beginning the approach you are proposing, and whether it is a new one (which parts?) or whether you are applying an existing one to solve ABSA. The initial part of Sect 4.1, that is, the core contribution of the paper (Semi-automatic Ontology Learning), it does not go deep enough describing the approach. Also an example would be useful to the reader. Further, in this semi-automatic approach is not clear which part is performed automatically and which one requires human intervention (and to which extent). For instance, where the ontology in Fig.4 come from? On the other hand, subsection "Hierarchical Relations" seems to clearly describe a completely automatic step in building the ontology, described in a reproducible manner. Experiments: comparing only against [19] as baseline may perhaps be fair but not useful to understand the effectiveness of the approach w.r.t. very different approaches to ABSA. Further, although table 2 shows that the approach let the user save some time constructing the ontology, it is not clear whether this is true in different domains. Also, the metodology to get this result is not described clearly. Concluding, it is a potentially interesting and valuable work but at this stage may require some extra efforts to improve readability. Minor: moreover, moreover, furthermore... too many close repetitions in Section 3.". }

• Paper.28_Review.0 hasContent "The paper presents a contribution on Aspect-based Sentiment Analysis, that is, sentiment analysis where the sentence may contains more opinions on a number of aspects, with potentially different polarity. The work is based/compares against the previous work of one of the authors. The previous approach, based on a given ontology exploited to improve the sentiment analysis task, is improved in this work through semi-automatic generation of the ontology based on external resources providing synsets. This work almost reaches the performance of the previous approach, but require less time to the user to construct the ontology due to the semi-automatic generation. The paper needs some extra work to improve the presentation, and it is difficult to read in some points. In the following some examples: On page 3 "finally the resulting ontology...", which ontology is it? Further, you are describing data (section 3) without describing your approach first. Regarding SemEval-2016 data, authors mention that has "a target "word, aspect, sentiment and sentiment score", but a map to terms used in Fig 1 would be beneficial (e.g. where is the sentiment score in the provided example?) "The term extraction method that is used has score based on domain pertinence (DP) and domain consensus (DC)" Is this part of a new contribution or you are just using results in [12]? In general it is not clear since the beginning the approach you are proposing, and whether it is a new one (which parts?) or whether you are applying an existing one to solve ABSA. The initial part of Sect 4.1, that is, the core contribution of the paper (Semi-automatic Ontology Learning), it does not go deep enough describing the approach. Also an example would be useful to the reader. Further, in this semi-automatic approach is not clear which part is performed automatically and which one requires human intervention (and to which extent). For instance, where the ontology in Fig.4 come from? On the other hand, subsection "Hierarchical Relations" seems to clearly describe a completely automatic step in building the ontology, described in a reproducible manner. Experiments: comparing only against [19] as baseline may perhaps be fair but not useful to understand the effectiveness of the approach w.r.t. very different approaches to ABSA. Further, although table 2 shows that the approach let the user save some time constructing the ontology, it is not clear whether this is true in different domains. Also, the metodology to get this result is not described clearly. Concluding, it is a potentially interesting and valuable work but at this stage may require some extra efforts to improve readability. Minor: moreover, moreover, furthermore... too many close repetitions in Section 3."".