Sentifiers: Interpreting Vague Intent Modifiers in Visual Analysis using Word Co-occurrence and Sentiment Analysis

TL;DR

Sentifiers is a system that interprets vague subjective modifiers in natural language data queries by analyzing word co-occurrence and sentiment, improving understanding in visual analysis tools.

Contribution

Introduces Sentifiers, a novel approach combining word co-occurrence and sentiment analysis to interpret vague predicates in natural language data queries.

Findings

The system effectively associates vague predicates with data attributes.

User interface supports interactive refinement of interpretations.

Qualitative evaluation shows usefulness in visual analysis tasks.

Abstract

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.