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

arXiv: https://arxiv.org/abs/2009.12701 · HTML: https://mlsanity.com/paper/2009.12701/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [A Generative AI System for Biomedical Data Discovery with Grammar-Based Visualizations](https://mlsanity.com/paper/2509.16454/) (2025)
- [YAC: Bridging Natural Language and Interactive Visual Exploration with Generative AI for Biomedical Data Discovery](https://mlsanity.com/paper/2509.19182/) (2025)

## Abstract (arXiv)

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.