IEEE Consumer Electronics Magazine - November/December 2021 - 95

with other sources of information, such as YouTube,
Facebook, or Instagram, could improve
the tool to help government authorities and
emergency responders in their decision-making
processes. Moreover, although our application
was specifically designed to address water
issues in Spain, the methodology described in
this article is also valid for other hazards, disasters,
and crisis situations, such as earthquakes,
wildfires, or even pandemics (e.g., COVID).
Indeed, this is a line of research that is currently
being explored. It should also be noted that we
aim to adapt WATERSensing to deal with other
languages, providing that we can make use of
two types of language-dependent resources, i.e.,
text-processing resources (e.g., POS tagger) and
lexical resources (e.g., polarity lexicon). In this
regard, since the former are readily available for
many European languages, the main effort is
expected to be placed on the latter. Nevertheless,
since the construction of semantic spaces
when setting topic categories is based on WordNet
synsets, further lexical resources are
reduced to a few datasets. Indeed, WATERSensing
has already been expanded to deal with
English microtexts.
ACKNOWLEDGMENTS
This work was supported in part by the Spanish
Ministry of Science and Innovation under Grant
RYC2018-025580-I, Grant RTI2018-096384-B-I00,
Grant RTC-2017-6389-5, and Grant RTC2019007159-5;
in part by the Fundacion Seneca del Centro
de Coordinacion de la Investigacion de la
Region de Murcia under Project 20813/PI/18; and
in part by the European Union's Horizon 2020
Research and Innovation Programme under Grant
101017861.
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