![](bg5.png)
Once the weight value w is calculated, for each
patterns for the two Neighborhood Lists, using an ap-
propriate value of threshold we remove all the pat-
terns with a value lower than a given threshold .
All the patterns remaining from the cut-off phase
represent the polarity variation rules we are looking
for and stored as elements p
i
in the two list of rules
P
+
= {p
i
+
} and P
−
= {p
i
−
}.
They are expressed as vector of 6 terms, someone
of which may be empty. These vectors, if used in sen-
timent analysis tasks, indicate, with a grade of proba-
bility, which terms must be present before and/or after
a subjective term to cause its polarity variation.
5 CONCLUSIONS AND FUTURE
WORK
We think that the work proposed in this paper could
be a plausible approach to determine and solve the
polarity variation problem in sentiment analysis ap-
plications. We are thus interested to develop this ap-
proach and we are working in this direction consider-
ing Google Play market store as resource for reviews
and SentiWordNet, a lexical resource for opinion min-
ing created by manual assignment of polarity to each
synset of Wordnet, as sentiment checker. This will let
us to define a set of valid rules to be integrated in a
computer aided system for real time detection of po-
larity variation as support for other systems.
The choice to work in this direction, in fact, is not
casual and arises from a real necessity occurred dur-
ing other sentiment analysis works were we are in-
volved. Just for example, during a sentiment anal-
ysis process done at word level on a large collec-
tion of Google Play market reviews, we have noticed
that SentiWordNet found more positive than negative
words inside reviews classified as negative with the
star rating system. Find any discordant polarity terms
inside a sentence it’s a typical situation, but not in the
numbers we found in our collection. This prompted
us to investigate and define a valid approach to solve
the problem.
ACKNOWLEDGEMENTS
This work has been partially supported by the POR
2007/2013 - Regione Siciliana - Misura 4.1.1.1. - IDS
(Innovative Document Sharing) Research Project and
by the PON01 01687 - SINTESYS (Security and IN-
TElligence SYSstem) Research Project.
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