Computational Intelligence - February 2014 - 22

❏ a method based on combining formal knowledge represen-

tation, natural language processing, and cognitively-founded
frames for opinion mining;
❏ an implementation of this method, named Sentilo1, which,
given an opinion sentence, returns its semantic representation annotated with opinion-relevant concepts;
❏ a model for opinion sentences that we use for annotating
their semantic representation.
The paper is structured as follows: Section 2 discusses relevant related work; Section 3 presents our method and describes
the opinion model; Section 4 describes Sentilo. Results and
evaluation of our approach are shown in Section 5 whereas
Section 6 drafts the conclusions and discusses future directions.
2. Related Work

Comprehensive surveys on opinion mining and sentiment
analysis are provided in literature by [9; 6; 29; 17; 23], hence
we do not attempt any additional survey. In this section, we
discuss works relevant to our approach, and list some of the
most popular lexical resources that are used for supporting
opinion mining.
Most existing SA approaches neglect the topic detection
task because they focus on the identification of sentiment features and on the analysis of the tone of an opinion. Several
works [28; 12; 39; 21] have considered the inclusion of topic
detection as an important issue for improving SA, however
instead of focusing on the topic detection task itself, and evaluating their approaches in this respect, they include topics within
their models, assuming that their identification is correct on a
statistical basis, and then evaluate the aggregate results from a
SA task perspective. Moreover, these approaches rely on the
presence of sentiment features for the identification of topics, as
their interest is on opinions that bear some explicit sentiment
expression, hence they do not consider the identification of
opinion topics in general.
As for opinion holder extraction, [40] compares three different methods based on classifiers for holder extraction and
proposes a novel method for addressing the less common case
of opinion holder playing a patient role. [25] uses semantic role
labeling based on FrameNet [34] for identifying holder and
topic in an opinion sentence focusing on a specific domain.
In this paper we focus on identifying holder and topics of an
opinion independently of the presence of sentiment features and
show that our approach is promising with respect to the more
general opinion mining task. With regards to popular resources
that are used for supporting opinion mining and SA, we have
considered (and partly used in the current version of our tool):
❏ SenticNet [7] is a publicly available semantic and a affective
resource for concept-level opinion and sentiment analysis.
SenticNet is built by means of sentic computing, a paradigm
that exploits both AI and Semantic Web techniques to better recognize, interpret, and process natural language opinions over the Web. We have integrated SenticNet in our
1

http://wit.istc.cnr.it/stlab-tools/sentilo

IEEE ComputatIonal IntEllIgEnCE magazInE | FEbruary 2014

implementation in order to retrieve the polarity scores of
terms expressing sentiment, which will be used in the next
evolution of Sentilo i.e., polarity classification of opinions
associated with their topics and sub-topics.
❏ SentiWordNet [2] is a lexical resource for opinion mining.
SentiWordNet assigns to each synset of WordNet three sentiment scores: positivity, negativity, objectivity. This resource
is integrated in Sentilo with the same aim as the integration
of SenticNet.
❏ VerbNet (VN) [5] is the largest on-line verb lexicon currently available for English. It is a hierarchical domainindependent, broad-coverage verb lexicon with mappings
to other lexical resources such as Word-Net, Xtag and
FrameNet.VerbNet is organized into (frame-like) verb classes
extending Levin's [27]classes through refinement and addition of subclasses to achieve syntactic and semantic coherence among members of a class. Each verb class is described
by "frames" consisting of a syntactic description and semantic predicates, with thematic roles and selectional restrictions
on the arguments. We exploit VerbNet in the frame-based
semantic representation of opinion sentences. In particular,
VerbNet thematic roles are a key reference in our approach
for the definition of heuristics that helps in detecting holders
and the topics of an opinion (see Section 4).
FrameNet [34] and LIWC [20] are additional examples of
available lexical resources that can be exploited in SA.
3. A Model for Sentic Computing

Adapting the definition from [24], we describe an opinion as an
intentional statement made by somebody (holder) on some fact
(topic), which is expressed with a possible sentiment.
Opinion sentences can refer to the holder of an opinion
either explicitly or implicitly. For example, in the sentence "I
believe the issue raised by the President is sufficiently important", the
holder of the sentiment is explicit and is denoted by "I". It can
be recognized thanks to the verb "believe", which also
announces that an opinion (possibly with some sentiment)
about a certain topic (i.e. the issue raised by the President) is
expressed elsewhere in the sentence. In the sentence "The issue
raised by the President is sufficiently important", the holder is
implicit (or externally identifiable, e.g. as the author of the text
or through other provenance mechanisms).
Similarly, the sentiment about a topic can be either implicit
or explicit. The above mentioned sentences express explicitly a
sentiment about the topic, however sometimes this does not
happen. For example, consider the sentence "He said that the
patient was suffering from tick bite fever". Again, the holder is
explicit (i.e. "he"), and the verb "said" announces that an opinion is to be expressed in the sentence, but the fact (i.e., the
patient was suffering from tick bite fever) is not associated with
an explicit sentiment.
Special attention has to be given to topics of an opinion. Sentiment analysis is also useful for supporting answers to questions
such as: given a topic, what is the trend of opinions about it? For
this reason, it is important to correctly identify topics of an

Table of Contents for the Digital Edition of Computational Intelligence - February 2014