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    We developed EBBE-Text, a tool enhancing neural network (NN) interpretability for binary text classification. It visualizes decision boundaries and data points, aiding understanding of NN behavior in natural language processing (NLP).

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    Area of Science:

    • Natural Language Processing (NLP)
    • Machine Learning
    • Data Visualization

    Background:

    • Neural networks (NNs) excel in NLP but lack interpretability.
    • Understanding NN decision-making in text classification is crucial.

    Purpose of the Study:

    • To introduce EBBE-Text, a novel tool for enhancing NN interpretability in binary text classification.
    • To provide interactive visualizations of NN decision boundaries and data representations.

    Main Methods:

    • Developed EBBE-Text, a visual platform integrating overview and local views of text representation spaces.
    • Incorporated interactive functionalities for exploring text data and classification results.
    • Utilized visualizations of NN representation spaces and classification information.

    Main Results:

    • A user study confirmed the effectiveness of the visual encoding.
    • A case study demonstrated the tool's utility in analyzing classifications from recent NNs on two datasets.
    • The tool enhances the interpretability of neural networks for text classification tasks.

    Conclusions:

    • EBBE-Text significantly improves the interpretability of neural networks in binary text classification.
    • The integrated visualization approach facilitates deeper understanding of NN behavior and classification outcomes.
    • The tool offers valuable insights for researchers and practitioners in NLP and machine learning.