Natural Language Processing (NLP) for Accessibility
The "Natural Language Processing (NLP) for Accessibility" initiative focuses on utilizing Natural Language Processing to analyze social media interactions and bug reports to enhance digital accessibility. This project aims to extract and synthesize insights from user-generated content to improve accessibility features in software applications, particularly for individuals with visual disabilities or eye conditions.
Motivation
Digital platforms and software updates frequently introduce changes that can inadvertently compromise accessibility. The motivation behind this project stems from the need to systematically analyze feedback from users, especially those with visual impairments, to identify and address these unintended consequences. By focusing on social media data and bug reports, the project leverages a rich source of real-time, user-generated insights that can guide developers in creating more inclusive technologies.
Project Goals
Accessibility Feedback Analysis: Develop NLP tools to automatically extract and classify accessibility-related feedback from social media and bug reports, focusing on visual disabilities and eye conditions.
Impact Assessment of Updates: Analyze how software updates affect app accessibility by studying user reviews and feedback over time, helping developers understand the ramifications of their modifications.
Interactive Reporting Tools: Create advanced NLP-driven reporting tools that allow developers to quickly identify common issues and trends in accessibility, enabling more responsive and informed development practices.
Enhancement of Accessibility Features: Use insights gained from NLP analysis to recommend specific changes and improvements in software design, aiming to mitigate barriers introduced by updates and ensure continuous accessibility compliance.
Charectrization
Our preliminary investigations have uncovered significant insights into the challenges and impacts of digital accessibility for individuals with visual impairments. These findings reveal that software updates, while intended to enhance functionality, can inadvertently introduce barriers for visually impaired users. By analyzing a wealth of user feedback, including accessibility reviews and bug reports across various platforms, we have identified recurring issues that affect the user experience. This analysis serves as a foundation for our NLP-driven initiatives, guiding our efforts to develop tools that can systematically address and rectify these accessibility concerns.
Human-Powered
Integrating human expertise with automated NLP analysis, this project involves collaboration with accessibility experts and the visually impaired community to refine the accuracy and relevance of the NLP tools. This approach ensures that the solutions developed are not only technologically sound but also practically beneficial for users with disabilities.
Accessibility Layers
By implementing a layered NLP framework, the project seeks to address various aspects of accessibility feedback analysis—from initial data collection to deep semantic analysis and actionable reporting. This structured approach ensures that each stage of feedback processing contributes to a comprehensive understanding of user experiences, ultimately leading to more accessible and inclusive software developments.
