Review Comment:
The paper proposes a Human Affective States Ontology (called HASO), represented in OWL, for modeling human emotions and sentiment. It is inspired by several psychological theories (e.g., Ekman, Cowie, OCC, etc.) and embeds many of the existing sentiment and emotion lexicons (e.g., WordNet, SenticNet, Harvard General Enquirer, etc.). The authors use Methontology for building the ontology, several tools to check its validity, and a custom modularization procedure in Protégé. Using a standard data set for tweets classifications and the SentiStength method of aggregating word sentiment, the authors show that the proposed ontology-based approach works better than some of the existing, classical machine learning methods.
The ontology seems to be very extensive ensuring a good coverage of the emotion and sentiment concepts. Also, based on the evaluation results, the authors show the usefulness of the proposed ontology. Nevertheless, it is not clear to me how the authors reconciled the various psychological theories and sentiment lexicons (how did you deal with redundancy and conflicts?). It is also not clear how the ontology relates to domain specific ontologies as proposed in [1,2,3] where complex axioms are able to capture domain specific sentiment (e.g., “cold” in “cold pizza” denotes negative sentiment, while “cold” in “cold beer” denotes positive sentiment). Such discussions need to be part of the related work.
[1] Mauro Dragoni, Soujanya Poria, Erik Cambria: OntoSenticNet: A Commonsense Ontology for Sentiment Analysis. IEEE Intelligent Systems 33(3): 77-85 (2018)
[2] Kim Schouten, Flavius Frasincar, Franciska de Jong: Ontology-Enhanced Aspect-Based Sentiment Analysis. ICWE 2017: 302-320
[3] Kim Schouten, Flavius Frasincar: Ontology-Driven Sentiment Analysis of Product and Service Aspects. ESWC 2018: 608-623
Why the deep learning solutions have been dismissed from the evaluation? I can imagine that these have a good performance and you might have good reasons for not using them in the comparison, but these reasons have to be given in the paper otherwise the evaluation becomes dubious. Also, you seem to be silent on the various senses a word can have, yes, you do classify a word under multiple concepts, but how do you know which concept to consider when processing text?
You seem to separate “recognition” from state without a direct link between them (see Figure 1). How do you know how to identify a particular state from a particular “recognition” (e.g., while there might be many words that denote mood, some denote bad mood while others denote good mood). You make use of HEO ontology (I assume this is the Human Emotion Ontology) but this is not properly introduced and referenced in the paper. Regarding the modularity task, should here not be some criterium on semantic cohesion when the modules are defined (counting the number of lines or axioms seems insufficient to me).
It is not clear why the sentiments associated with the emojis from reference [25] (paper) available at http://people.few.eur.nl/hogenboom/files/EmoticonSentimentLexicon.zip were not directly used (instead of converting the emojis to their word equivalent expressions and then determining the sentiment), is your solution working better (some analysis needs to be given)? Which were the used SPARQL queries?
The paper misses numerous references, e.g., HEO, Ekman model, Douglas-Cowie category model, OCC model, Drummond emotion vocabulary, Frijda Action Tendency model, NeOn dictionary, etc. There are several style issues present sometimes in the paper: there should be a space before/after references in text (unless they are the last token), there should be no space before a footnote number, there should be no space before a “.” or “,” , etc.
Other comments:
-page 2: “How Net dictionary”, which Net dictionary do you mean?
-page 8: ill formed sentence starting with small letter “Since recognizing the need…”
-page 9: “we divide” instead of “We divide”
-page 10: “which is clarified” instead of “which clarified”
-page 10: “Figure shows”, which figure?
-page 12: ill formed sentence “...have different representation in another domain and context [28].”
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