Computational Intelligence - February 2014 - 30

case, since we have started only with the rules based on the
formal definitions given in 2 and 3 (cf. Section 3).
6. Conclusion and Future Work

As described in [17], the current state of art in sentiment
analysis and opinion mining suffers from simplistic techniques, which leave several challenges, including: (a) compositionality of opinion, (b) opinionated entity disambiguation,
(c) contextualization of sentiment, (d) dealing with sarcasm,
(e) noise in text, and (f) factual sentiment.
In this paper we have described a method and a tool for
opinion holder and topic detection, which are crucial for (a),
(c), and (f). Heuristic graph mining on top of a machine
reader that converts sentences into RDF-OWL graphs proves
to be a good idea for taking advantage of the relational features existing in the semantic structure of an opinionated text.
Results are excellent for holder detection and good for topic/
subtopic detection, and improvement seems easily attainable
by incrementally refining heuristics for topic and subtopic
detection. Crowdsourcing techniques can be also applied to
this task.
In addition, the use of a semantic-web-aware machine
reader like FRED makes it simple to solve (b), at least for
public entities and concepts that can be resolved on resources
like DBpedia and WordNet. FRED foundation on cognitive
frames is also handy for attempting (future work) a resolution
of (d), based e.g. on formalizing AffectiveSpace [8] knowledge
into frame-based representations.
Ongoing work concentrates on extracting opinion features
from all graph patterns that are generated by Sentilo, and on
designing an algorithm to calculate aspect-based opinion scores.
The algorithm is being implemented, and will be functionally
evaluated by comparison to state-of-art sentiment analysis tools.
Moreover, computational intelligence methods (cf. Fig. 4),
including fuzzy reasoning, combinatorial optimization on graph mining,
learning mechanisms, sentic pattern discovery and analogical reasoning,
described in Section 4, are being developed on top of Sentilo.
Acknowledgment

The work described in this paper was performed with the support of the PRISMA (PiattafoRme cloud Interoperabili per
SMArt-government) project, funded by the MIUR (Ministero
dell'Istruzione, dell'Università e della Ricerca).
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Table of Contents for the Digital Edition of Computational Intelligence - February 2014
