Feature-Based Visual Sentiment Analysis of Text Document Streams

Abstract

This article describes automatic methods and interactive visualizations that are tightly coupled with the goal to enable users to detect interesting portions of text document streams. In this scenario the interestingness is derived from the sentiment, temporal density, and context coherence that comments about features for different targets (e.g., persons, institutions, product attributes, topics, etc.) have. Contributions are made at different stages of the visual analytics pipeline, including novel ways to visualize salient temporal accumulations for further exploration. Moreover, based on the visualization, an automatic algorithm aims to detect and preselect interesting time interval patterns for different features in order to guide analysts. The main target group for the suggested methods are business analysts who want to explore time-stamped customer feedback to detect critical issues. Finally, application case studies on two different datasets and scenarios are conducted and an extensive evaluation is provided for the presented intelligent visual interface for feature-based sentiment exploration over time.

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Index Terms

• Applied computing

  • Document management and text processing

    • Document capture

      • Document analysis

  • Enterprise computing

• Computing methodologies

  • Artificial intelligence

    • Natural language processing

      • Language resources

    • Philosophical/theoretical foundations of artificial intelligence

      • Cognitive science

  • Computer graphics

    • Graphics systems and interfaces

• Human-centered computing

  • Human computer interaction (HCI)

    • Interaction devices

      • Graphics input devices

    • Interaction paradigms

      • Graphical user interfaces

    • Interactive systems and tools

• Information systems

  • Information retrieval

    • Document representation

      • Content analysis and feature selection

  • Information systems applications