  The relationship between Facebook posts and the corresponding reaction
feature is an interesting subject to explore and understand. To achieve this
end, we test state-of-the-art Sinhala sentiment analysis models against a data
set containing a decade worth of Sinhala posts with millions of reactions. For
the purpose of establishing benchmarks and with the goal of identifying the
best model for Sinhala sentiment analysis, we also test, on the same data set
configuration, other deep learning models catered for sentiment analysis. In
this study we report that the 3 layer Bidirectional LSTM model achieves an F1
score of 84.58% for Sinhala sentiment analysis, surpassing the current
state-of-the-art model; Capsule B, which only manages to get an F1 score of
82.04%. Further, since all the deep learning models show F1 scores above 75% we
conclude that it is safe to claim that Facebook reactions are suitable to
predict the sentiment of a text.
