Computational Intelligence - February 2014 - 28

Holder Activation

Holder Positive Polarity

Declare Verbs
Pos-Conj-Cert Verbs
Pos-Conj-Doubt Verbs
Transfer-of-Message Verbs
Tell Verbs
Chitchat Verbs
Say Verbs

Positively Characterized Verbs

Holder Activation, Negated Holder Negative Polarity
Neg-CoNj Verbs
Negatively Characterized Verbs

Figure 8 Levin opinion : revision of Levin's classification of verbs based
on their relevance for the opinion mining and sentiment analysis
tasks. We define four categories: holder activation verbs, holder activation negative verbs, holder positive polarity verbs, and holder negative polarity verbs.

❏ Holder activation negated verbs: verbs such as contest, disagree,

dismiss, oppose, refuse, reject, etc. which indicate the presence of
an opinion holder, who is the subject of the underlying
verb; subjects of such verbs have an opinion which is in
contrast with whatever is expressed in the opinionated context. Basically, this class of verbs is equivalent to the first class
when a negation such as do not, never, etc. is present;
❏ Holder positive polarity verbs: verbs such as love, like, honor,
support, etc.. These verbs indicate the presence of an opinion holder expressing a positive sentiment on some
topic(s). Verbs in this category appear in the opinion trigger context and have an impact on the opinionated contexts (Section 3.2);
❏ Holder negative polarity verbs: verbs such as dislike, hate, etc..
These verbs indicate the presence of an opinion holder
expressing a negative sentiment on some topic(s). This
class of verbs is equivalent to the previous one when a
negation occurs.Verbs in this category appear in the opinion trigger context and impact on the opinionated contexts (Section 3.2).
Based on our Davidson's theory-based assumption, a verb in
a sentence denotes an occurrence of an event, which is participated in by a number of entities, each having a specic role: if a
trigger event is detected in a sentence, its participating agent is
annotated as the opinion holder.
In our sample sentence "Drug dealers have lost some 3,600
kilos of narcotics, the colonel said", the verb said is detected as an
holder activation verb, hence its associated event instance
fred:say_1 is used for identifying the opinion holder that is
the entity fred:colonel_1). This is obtained by recognizing the triple pattern:
fred:say_1 vn.role:Agent ?holder .

It might happen that a same verb is classified in more than
one category. For example, the verb accept belongs to both
Holder activation verbs and Holder positive polarity verbs as it both

IEEE ComputatIonal IntEllIgEnCE magazInE | FEbruary 2014

implies the presence of an holder and the presence of a positive
sentiment on a certain topic. This information will influence
the computation of sentiment scores associated with the opinion topics, a task that we leave for future development.
topic Detection
As for topic detection, Sentilo distinguishes main topics from
sub-topics of an opinion. To enable this capability we have
defined two equivalent classes of VerbNet thematic roles: a
class named AGT, which includes all agentive roles, and a class
named PTT, which includes all passive roles. Sentilo annotates
as main topics those entities that participate in a trigger event
playing a PTT role. If the sentence misses a trigger event, all
entities (modulo some exceptions) having only outgoing arcs
in the graph (roots) are annotated as main topics.
Recognizing sub-topics is a more complex and sophisticated task. Given an opinion expressed on an event, such opinion can convey an indirect opinion about some of its participating entities. For example, the sentence: I am happy that
Berlusconi has been condemned for fiscal evasion. expresses an opinion (positive) on the condemnation event, and indirectly it
conveys an opinion (negative) on Berlusconi, while it does not
express an opinion on the specific motivation of the condemnation. Hence, in this case "Berlusconi" should be annotated as
a sub-topic, while "fiscal evasion" should not. We say that only
entities that play sensitive roles in an event are sub-topics.9
Establishing if a role is sensitive for an event depends on the
specific context. However, we have empirically observed that
the roles belonging to AGT and PTT are sensitive in most of
the cases. Based on this rationale, our heuristics for annotating
sub-topics refines the Formula 3 (cf. Section 3.2) as defined by
the following Formula 4.
(Topic -(7 (AGT . PTT) (dul: Event - MainTopic)))
(4)
5 SubTopic
When a main topic is an event, entities that participate in it
having either an agentive role or a passive role are sub-topics.
upcoming Extensions
The following extensions to the system are currently under
study:
❏ Sentiment score computation, which uses both the scores provided by SenticNet and SentiWordNet and the semantics of
the representation model to assess sentiment scores to the
detected topics.
❏ Topic co-reference resolution, which deals with the synonymy
between entities or topics, where the goal is to recognize
the same situations or events denoted using different expressions, using similarity methods combined with semantic
models produced using FRED.
9

However, future work should take into account contextual effects on sensitivity. For
example, the sentence I am happy that Berlusconi has been condemned precisely for
fiscal evasion. contains a modifier that shifts sensitivity also to the oblique role filled by
"fiscal evasion".

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