  During the course of a Humanitarian Assistance-Disaster Relief (HADR) crisis,
that can happen anywhere in the world, real-time information is often posted
online by the people in need of help which, in turn, can be used by different
stakeholders involved with management of the crisis. Automated processing of
such posts can considerably improve the effectiveness of such efforts; for
example, understanding the aggregated emotion from affected populations in
specific areas may help inform decision-makers on how to best allocate
resources for an effective disaster response. However, these efforts may be
severely limited by the availability of resources for the local language. The
ongoing DARPA project Low Resource Languages for Emergent Incidents (LORELEI)
aims to further language processing technologies for low resource languages in
the context of such a humanitarian crisis. In this work, we describe our
submission for the 2019 Sentiment, Emotion and Cognitive state (SEC) pilot task
of the LORELEI project. We describe a collection of sentiment analysis systems
included in our submission along with the features extracted. Our fielded
systems obtained the best results in both English and Spanish language
evaluations of the SEC pilot task.
