Annotating WordNet synsets by sentiment-related information: issues and potential solutions

Many works in sentiment analysis have focused on the problem of subjectivity detection, at various levels: from terms (or term senses), as in the automatic annotation of lexical resources, to fragments of text, as in opinion extraction, to entire documents, as in sentiment classification. At all these levels, the two dimensions that have been investigated more actively are polarity ("positive/negative") and force ("strong/mild/weak" expression of positivity or negativity). In the SentiWordNet project we made a first attempt at automatically adding information concerning these two dimensions to WordNet. In another, more recent research we have explored a further dimension of subjective language, i.e, attitude type, which distinguishes, for example, between moral appreciation ("honest") and aesthetic appreciation ("beautiful"). We think that endowing WordNet with annotations pertaining to these three dimensions (polarity + force + attitude type) would make WordNet an even more invaluable resource for sentiment analysis. Adding this information to WordNet would not be an easy task, for at least two reasons. One is the sheer size of the resource; this might call, at least initially, for a semi-automatic approach, on the line of the SentiWordnet or of the "WordNet Evocation" projects. The other is the choice of the taxonomy of sentiment types, which needs to compromise between conceptual subtlety and real-world applicability. For our recent work on attitude type we have adopted a taxonomy of attitude types originally defined in Martin and White's Appraisal Theory; however, other potentially interesting alternatives have been developed, e.g. in the EU-funded Simple project. However, we conjecture that even this three-dimensional specification of the sentiment-related properties of synsets might not be sufficient for application purposes, at least for some parts of speech. For example, it is conceivable that a verb's polarity should not be characterized as positive or negative tout court, but that a distinction should be made as to which semantic role of the verb such polarity is bestowed upon. For instance, the verbs "torture" and "discard" both have a negative slant; however, while "torture" casts a negative character on the subject of the action (and on the action itself), "discard" typically casts a negative character on the direct object of the action. Such distinctions should be accounted for in a lexicon, especially in order to make it useful for opinion extraction applications.