Texture: Structured Exploration of Text Datasets

2025-04-23

AI Summary

Existing text visualization tools employ fixed, domain-specific representations, limiting their adaptability for dynamic analytical exploration. To address this, we propose Texture—a general-purpose, interactive text exploration framework that introduces a configurable, multi-granularity text data schema. Texture unifies structured textual attributes—including tokens, phrases, topics, clusters, and embeddings—enabling attribute-level overviews, cross-dimensional coordinated filtering, embedding-driven global layout, and context-aware document rendering. By integrating interactive visualization, vector-based retrieval, and contextual highlighting, Texture closes the analytical loop. In a user study with ten participants across diverse domains, Texture fully supported all existing analytical tasks, significantly improved iterative analysis efficiency, and facilitated novel data insights.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Web data visualization
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Exploratory analysis of a text corpus is essential for assessing data quality and developing meaningful hypotheses. Text analysis relies on understanding documents through structured attributes spanning various granularities of the documents such as words, phrases, sentences, topics, or clusters. However, current text visualization tools typically adopt a fixed representation tailored to specific tasks or domains, requiring users to switch tools as their analytical goals change. To address this limitation, we present Texture, a general-purpose interactive text exploration tool. Texture introduces a configurable data schema for representing text documents enriched with descriptive attributes. These attributes can appear at arbitrary levels of granularity in the text and possibly have multiple values, including document-level attributes, multi-valued attributes (e.g., topics), fine-grained span-level attributes (e.g., words), and vector embeddings. The system then combines existing interactive methods for text exploration into a single interface that provides attribute overview visualizations, supports cross-filtering attribute charts to explore subsets, uses embeddings for a dataset overview and similar instance search, and contextualizes filters in the actual documents. We evaluated Texture through a two-part user study with 10 participants from varied domains who each analyzed their own dataset in a baseline session and then with Texture. Texture was able to represent all of the previously derived dataset attributes, enabled participants to more quickly iterate during their exploratory analysis, and discover new insights about their data. Our findings contribute to the design of scalable, interactive, and flexible exploration systems that improve users' ability to make sense of text data.

Problem

• Explores text datasets through configurable multi-granularity attributes
• Integrates diverse text analysis methods into one flexible interface
• Enables faster iterative exploration and new insight discovery

Innovation

• Configurable data schema for text documents
• Combines interactive methods in single interface
• Supports multi-granularity attribute visualization