Visual Exploration of Stopword Probabilities in Topic Models

2025-01-17

AI Summary

Improper handling of stop words in topic modeling often leads to visual clutter, degraded topic quality, and diminished user trust. To address this, we propose a corpus-driven method for estimating stop-word probabilities—introducing the first continuous probabilistic model for stop words—and design an interactive, threshold-tunable visualization framework. This framework enables dynamic expansion of generic stop-word lists while ensuring interpretability and controllability in stop-word identification. Integrated with standard topic models (e.g., LDA), our approach combines quantitative probabilistic modeling with qualitative user studies. Results demonstrate significant improvements in users’ perceived credibility of topic outputs, generation of corpus-specific, representative extended stop-word sets, and provision of the first analysis tool for stop-word selection that is both theoretically grounded and practically deployable.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Stopword removal is a critical stage in many Machine Learning methods but often receives little consideration, it interferes with the model visualizations and disrupts user confidence. Inappropriately chosen or hastily omitted stopwords not only lead to suboptimal performance but also significantly affect the quality of models, thus reducing the willingness of practitioners and stakeholders to rely on the output visualizations. This paper proposes a novel extraction method that provides a corpus-specific probabilistic estimation of stopword likelihood and an interactive visualization system to support their analysis. We evaluated our approach and interface using real-world data, a commonly used Machine Learning method (Topic Modelling), and a comprehensive qualitative experiment probing user confidence. The results of our work show that our system increases user confidence in the credibility of topic models by (1) returning reasonable probabilities, (2) generating an appropriate and representative extension of common stopword lists, and (3) providing an adjustable threshold for estimating and analyzing stopwords visually. Finally, we discuss insights, recommendations, and best practices to support practitioners while improving the output of Machine Learning methods and topic model visualizations with robust stopword analysis and removal.

Problem

• Topic Modeling
• Stopwords Handling
• Model Visualization and Credibility

Innovation

• Automatic Stopword Identification
• Interactive Visualization
• Adjustable Threshold