Human online activities leave digital traces that provide a perfect opportunity to understand their behavior better. Social media is an excellent place to spark conversations or state opinions. Thus, it generates large-scale textual data. In this paper, we harness those data to support the
effort of personality measurement. Our first contribution is to develop the Big Five personality traitbased model to detect human personalities from their textual data in the Indonesian language. The
model uses an ontology approach instead of the more famous machine learning model. The former
better captures the meaning and intention of phrases and words in the domain of human personality. The legacy and more thorough ways to assess nature are by doing interviews or by giving questionnaires. Still, there are many real-life applications where we need to possess an alternative
method, which is cheaper and faster than the legacy methodology to select individuals based on
their personality. The second contribution is to support the model implementation by building a
personality measurement platform. We use two distinct features for the model: an n-gram sorting
algorithm to parse the textual data and a crowdsourcing mechanism that facilitates public involvement contributing to the ontology corpus addition and filtering.