Agent-based visualization of streaming text

2025-07-10

AI Summary

To address the challenge of intuitively visualizing topic evolution and word co-occurrence dynamics in streaming text, this paper proposes an agent-based dynamic visualization method. Keywords are modeled as adaptive agents whose sizes encode term frequencies, spatial clustering reflects real-time co-occurrence strength, and area intersection ratios quantify the coupling between co-occurrence and frequency. A stability-enhanced dynamic layout algorithm ensures visual continuity and readability under incremental data updates. The system integrates streaming data ingestion (news, blogs, social media), online search API–driven topic focusing, and a lightweight web backend. Experiments demonstrate that the approach accurately reveals topic emergence, decay, and migration, generating interpretable, interactive, low-latency semantic clusters. It significantly improves both timeliness and comprehensibility of streaming text topic analysis while preserving visual stability.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

We present a visualization infrastructure that maps data elements to agents, which have behaviors parameterized by those elements. Dynamic visualizations emerge as the agents change position, alter appearance and respond to one other. Agents move to minimize the difference between displayed agent-to-agent distances, and an input matrix of ideal distances. Our current application is visualization of streaming text. Each agent represents a significant word, visualizing it by displaying the word itself, centered in a circle sized by the frequency of word occurrence. We derive the ideal distance matrix from word cooccurrence, mapping higher co-occurrence to lower distance. To depict co-occurrence in its textual context, the ratio of intersection to circle area approximates the ratio of word co-occurrence to frequency. A networked backend process gathers articles from news feeds, blogs, Digg or Twitter, exploiting online search APIs to focus on user-chosen topics. Resulting visuals reveal the primary topics in text streams as clusters, with agent-based layout moving without instability as data streams change dynamically.

Problem

• Visualizing streaming text data using agent-based dynamic clusters
• Mapping word co-occurrence to agent distances for topic representation
• Real-time layout adaptation to changing text streams without instability

Innovation

• Agent-based dynamic visualization of text streams
• Distance matrix derived from word co-occurrence
• Networked backend gathers and focuses on topics