  The Facebook network allows its users to record their reactions to text via a
typology of emotions. This network, taken at scale, is therefore a prime data
set of annotated sentiment data. This paper uses millions of such reactions,
derived from a decade worth of Facebook post data centred around a Sri Lankan
context, to model an eye of the beholder approach to sentiment detection for
online Sinhala textual content. Three different sentiment analysis models are
built, taking into account a limited subset of reactions, all reactions, and
another that derives a positive/negative star rating value. The efficacy of
these models in capturing the reactions of the observers are then computed and
discussed. The analysis reveals that binary classification of reactions, for
Sinhala content, is significantly more accurate than the other approaches.
Furthermore, the inclusion of the like reaction hinders the capability of
accurately predicting other reactions.
