  Natural language interaction with data visualization tools often involves the
use of vague subjective modifiers in utterances such as "show me the sectors
that are performing" and "where is a good neighborhood to buy a house?."
Interpreting these modifiers is often difficult for these tools because their
meanings lack clear semantics and are in part defined by context and personal
user preferences. This paper presents a system called \system that makes a
first step in better understanding these vague predicates. The algorithm
employs word co-occurrence and sentiment analysis to determine which data
attributes and filters ranges to associate with the vague predicates. The
provenance results from the algorithm are exposed to the user as interactive
text that can be repaired and refined. We conduct a qualitative evaluation of
the Sentifiers system that indicates the usefulness of the interface as well as
opportunities for better supporting subjective utterances in visual analysis
tasks through natural language.
