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The TechBeat: Leveraging MinIO and Apache Tika for Automated Text Extraction and Analysis (4/26/2024)

The TechBeat: Leveraging MinIO and Apache Tika for Automated Text Extraction and Analysis (4/24/2024)

Leveraging MinIO and Apache Tika for Automated Text Extraction and Analysis

স্বয়ংক্রিয় পাঠ্য নিষ্কাশন এবং বিশ্লেষণের জন্য MinIO এবং Apache Tika ব্যবহার করা

Nutzung von MinIO und Apache Tika zur automatischen Textextraktion und -analyse

Aprovechando MinIO y Apache Tika para la extracción y el análisis de texto automatizados

Tirer parti de MinIO et Apache Tika pour l’extraction et l’analyse automatisées de texte

स्वचालित पाठ निष्कर्षण और विश्लेषण के लिए मिनियो और अपाचे टिका का लाभ उठाना

MinIO と Apache Tika を活用した自動テキスト抽出と分析

자동화된 텍스트 추출 및 분석을 위해 MinIO 및 Apache Tika 활용

Aproveitando MinIO e Apache Tika para extração e análise automatizada de texto

Использование MinIO и Apache Tika для автоматического извлечения и анализа текста

Otomatik Metin Çıkarma ve Analizi için MinIO ve Apache Tika'dan Yararlanma

Tận dụng MinIO và Apache Tika để trích xuất và phân tích văn bản tự động

利用 MinIO 和 Apache Tika 进行自动文本提取和分析

This Article Is Worth Your Attention if You Are passionate about writing good quality software and want to enhance the stability of your app with tests. Are tired of unexpected bugs popping up in your production systems. Need help understanding what automated tests are and how to approach them. Why Do We Need Automated Tests? As engineers, we want to , but with each new feature we create, we inevitably increase the size and complexity of our apps. build things that work As the product grows, it becomes more and more time-consuming to (e.g., with your hands) test every functionality affected by your changes. manually The absence of automated tests leads to us either spending too much time and slowing our shipping speed down or spending too little to save the velocity, resulting in new bugs in the backlog along with the late-night calls from PagerDuty. On the contrary, . So, computers can be programmed to do the same repeatedly let's delegate testing to computers! Types of Tests The suggests . Let's dive deep into every kind and understand why we need each. Testing pyramid idea three main types of tests: unit, integration, and end-to-end Unit Tests A is a small piece of logic you test in (without relying on other components). unit isolation They finish within seconds. allows them to run them at any point in time, locally and on CI, without spinning up the dependent services/making API and database calls. Unit tests are fast. Isolation A function that accepts two numbers and sums them together. We want to call it with different arguments and assert that the returned value is correct. Unit test example: // Function "sum" is the unit
const sum = (x, y) => x + y

test('sums numbers', () => {
  // Call the function, record the result
  const result = sum(1, 2);

  // Assert the result
  expect(result).toBe(3)
})

test('sums numbers', () => {
  // Call the function, record the result
  const result = sum(5, 10);

  // Assert the result
  expect(result).toBe(15)
}) A more interesting example is the React component that renders some text after the API request is finished. We need to mock the API module to return the necessary values for our tests, render the component, and assert the rendered HTML has the content we need. // "MyComponent" is the unit
const MyComponent = () => {
  const { isLoading } = apiModule.useSomeApiCall();

  return isLoading ? <div>Loading...</div> : <div>Hello world</div>
}

test('renders loading spinner when loading', () => {
  // Mocking the API module, so that it returns the value we need
  jest.mock(apiModule).mockReturnValue(() => ({
    useSomeApiCall: jest.fn(() => ({
      // Return "isLoading: true" for this test case
      isLoading: true
    }))
  }))

  // Execute the unit (render the component)
  const result = render(<MyComponent />)

  // Assert the result
  result.findByText('Loading...').toBeInTheDocument()
})

test('renders text content when not loading', () => {
  // Mocking the API module
  jest.mock(apiModule).mockReturnValue(() => ({
    useSomeApiCall: jest.fn(() => ({
      // Return "isLoading: false" for this test case
      isLoading: false
    }))
  }))

  // Execute the unit (render the component)
  const result = render(<MyComponent />)

  // Assert the result
  result.findByText('Hello world').toBeInTheDocument()
}) Integration Tests When your interacts with other , we call it an . These tests are slower than unit tests, but they test how the parts of your app connect. unit units (dependencies) integration A service that creates users in a database. This requires a DB instance ( ) to be available when the tests are executed. We will test that the service can create and retrieve a user from the DB. Integration test example: dependency import db from 'db'

// We will be testing "createUser" and "getUser"
const createUser = name => db.createUser(name) // creates a user
const getUser = name => db.getUserOrNull(name) // retrieves a user or null

test("creates and retrieves users", () => {
  // Try to get a user that doesn't exist, assert Null is returned
  const nonExistingUser = getUser("i don't exist")
  expect(nonExistingUser).toBe(null);

  // Create a user
  const userName = "test-user"
  createUser(userName);

  // Get the user that was just created, assert it's not Null
  const user = getUser(userName);
  expect(user).to.not.be(null)
}) End-to-End Tests It's an test when we test the , where all its dependencies are available. Those tests best simulate actual user behavior and allow you to catch in your app, but they are the type of tests. end-to-end fully deployed app all possible issues slowest Whenever you want to run end-to-end tests, you must provision all the infrastructure and make sure 3rd party providers are available in your environment. You want to have them for the features of your app. only mission-critical Login flow. We want to go to the app, fill in the login details, submit it, and see the welcome message. Let's take a look at an end-to-end test example: test('user can log in', () => {
  // Visit the login page
  page.goto('https://example.com/login');

  // Fill in the login form
  page.fill('#username', 'john');
  page.fill('#password', 'some-password');

  // Click the login button
  page.click('#login-button');

  // Assert the welcome message is visible
  page.assertTextVisible('Welcome, John!')
}) How Do You Choose What Kind of Test to Write? Remember that , and tests. end-to-end tests are slower than integration integration tests are slower than unit If the feature you are working on is mission-critical, consider writing at least one test (such as checking how the Login functionality works when developing the Authentication flow). end-to-end Besides mission-critical flows, we want to test as many edge cases and various states of the feature as possible. allow us to test how the parts of the app work together. Integration tests Endpoints should perform the operations, produce the expected result, and not throw any unexpected errors. Having integration tests for endpoints and client components is a good idea. Client components should display the correct content and respond to user interactions with how you expect them to respond. And finally, when should we choose ? All the small functions that can be tested in isolation, such as that sums the numbers, that renders tag, are great candidates for unit tests. Units are perfect if you follow the approach. unit tests sum Button <button> Test Driven Development What's Next? (but start small) Write some tests! that suits your project/language. Each language has a popular library for testing, such as / for JavaScript, / for end-to-end (uses JavaScript as well), for Java, etc. Install a testing framework Jest Vitest Cypress Playwright JUnit Find a small function in your project and write a test for it. unit Write an test for some component/service-database interaction. integration Choose a critical scenario that can be quickly tested, such as a simple login flow, and write an test for that. end-to-end Do the things above once to understand how it works. Then, do it again during some feature/bug work. Then share it with your colleagues so that you all write tests, save time, and sleep better at night! Useful Resources: My personal blog by The Practical Test Pyramid Ham Vocke by Test Driven Development Martin Fowler

Stable Software: The Power of Automated Tests

স্থিতিশীল সফ্টওয়্যার: স্বয়ংক্রিয় পরীক্ষার শক্তি সম্পর্কে জানুন

Stabile Software: Erfahren Sie mehr über die Leistungsfähigkeit automatisierter Tests

Software estable: conozca el poder de las pruebas automatizadas

Logiciel stable : découvrez la puissance des tests automatisés

स्थिर सॉफ़्टवेयर: स्वचालित परीक्षणों की शक्ति के बारे में जानें

安定したソフトウェア: 自動テストの威力について学ぶ

안정적인 소프트웨어: 자동화된 테스트의 힘에 대해 알아보세요

Software estável: aprenda sobre o poder dos testes automatizados

Стабильное программное обеспечение: узнайте о возможностях автоматизированных тестов

Kararlı Yazılım: Otomatik Testlerin Gücünü Öğrenin

Phần mềm ổn định: Tìm hiểu về sức mạnh của kiểm thử tự động

稳定的软件：了解自动化测试的力量

What Are Code Explainers? Code explainers are tools designed to make code more comprehensible. They work by breaking down complex lines of code into simpler, understandable chunks. They can highlight the logic behind each code block and depict the flow of data throughout the program. The ability to explain code is essential for software development teams to understand the functionality of the code, , and . carry out debugging collaborate efficiently takes this a step further. Automated code explainers are advanced tools that use various techniques, including AI and machine learning, to automatically interpret and explain the code. They not only explain the code but also suggest improvements and detect potential errors. This automation can transform and scale up the code review process. Automating code explainers Types of Automated Code Explainers Static Code Analysis Tools Static code analysis tools are software applications that examine source code without executing it. They identify potential errors, security vulnerabilities, and coding standard violations. These tools are effective for maintaining code quality and ensuring adherence to coding standards. They analyze the code for common programming errors, such as syntax errors, logical errors, and potential bugs. Static analysis tools can also check for compliance with coding conventions and guidelines, helping to maintain a consistent codebase. By using static analysis tools, developers can detect and fix problems early in the development process. This proactive approach reduces the likelihood of bugs and vulnerabilities in the final product. Additionally, these tools can automate the tedious and error-prone process of code review, enabling developers to focus on more complex tasks. Integration of static analysis tools into the continuous integration/continuous deployment (CI/CD) pipeline ensures continuous code quality checks throughout the development lifecycle. Code Visualization Tools Code visualization tools offer a graphical representation of the codebase, making it easier to understand complex code structures and relationships. They can display various aspects of the software, like class hierarchies, dependency graphs, and control flow diagrams. This visual approach helps developers comprehend the architecture and design patterns within the code, aiding in better decision-making during development and maintenance. These tools are particularly useful in large projects with intricate codebases, as they provide a macroscopic view of the software architecture. Visualization assists in identifying redundant or tightly coupled components, enabling refactoring efforts to improve code quality and maintainability. Code visualization tools also facilitate new team members' understanding of the existing codebase, speeding up the onboarding process. By integrating these tools into the development environment, teams can continuously monitor and analyze the evolving structure of their software. Automated Code Review Tools Automated code review tools are designed to analyze source code and suggest improvements, similar to a human reviewer. They check the code for common issues like coding standard violations, potential bugs, and performance issues. These tools use a combination of static code analysis, pattern recognition, and more recently, large language models (LLMs), to evaluate code quality and consistency. The use of automated code review tools streamlines the code review process, allowing teams to focus on more complex and subjective aspects of code quality. They also facilitate consistent code reviews across teams, reducing the variability that can occur with manual reviews. Integrating these tools into the development workflow can significantly speed up the review process and improve the overall quality of the codebase. Furthermore, automated reviews provide immediate feedback to developers, fostering a culture of continuous improvement and learning. AI Coding Assistants AI coding assistants use artificial intelligence and machine learning, most notably LLMs, to assist developers in writing code. They can suggest code completions, identify errors, and offer recommendations for code optimization. These assistants learn from a vast repository of code examples and adapt to the coding style and preferences of the developer. They can significantly speed up the coding process and reduce the likelihood of errors. AI coding assistants are particularly useful for repetitive coding tasks and boilerplate code, allowing developers to focus on more complex and creative aspects of software development. They also serve as an educational tool for novice developers, providing real-time feedback and suggestions. By integrating AI coding assistants into the development environment, developers can enhance their productivity and code quality. These assistants are continually evolving, becoming more sophisticated and helpful as they learn from more data and user interactions. When used as code explainers, AI coding assistants can dissect code line by line, providing explanations in plain language. They can clarify the purpose of specific functions, variables, and algorithms, making it easier for developers to grasp the underlying logic. This capability is invaluable for learning and understanding new codebases or programming paradigms. For example, when encountering an unfamiliar function or method, a developer can use an AI assistant to get an instant explanation of its purpose and usage, along with examples of similar code patterns. Benefits of Using Automated Code Explainers Efficiency in Understanding Code Automated code explainers can break down complicated code into simpler parts, making it easier for developers to grasp. They can also trace the flow of data and logic across the program, giving a visual representation of the code's functionality. Automated code explainers can also detect potential errors and bugs in the code. By using advanced analysis techniques and machine learning algorithms, they can foresee issues that might not be apparent to the human eye. This can save developers the time they would otherwise spend troubleshooting and debugging. Improved Collaboration Automated code explainers significantly improve collaboration among team members. By providing clear and concise explanations of the code, they ensure all team members have a shared understanding of the code. This shared understanding fosters better communication and collaboration among the team. Additionally, automated code explainers can serve as a neutral third party during code reviews. They can provide unbiased feedback on the code, preventing any personal biases from influencing the review process. This unbiased review promotes a fair and collaborative environment where the focus remains on improving the code. Learning and Development Automated code explainers are excellent learning tools for developers. They provide real-time feedback on the code, allowing developers to learn and improve as they code. Developers can see the impact of their code changes immediately, understanding what works and what doesn't. Moreover, for junior developers or those learning a new programming language, automated code explainers can serve as a valuable guide. They can help these developers understand complex code structures and patterns, accelerating their learning process. 5 Best Practices for Using Code Explainers Effectively 1. Integrate with Development Workflow Code explainers should be a part of your daily coding routine. For example, you can set up your explainer tool to automatically analyze your code whenever you commit changes. This approach ensures that you receive feedback on your code in real-time, allowing you to address any issues or inefficiencies promptly. 2. Regularly Review and Refine Suggestions Code explainers use static rules and machine learning algorithms to analyze your code and provide suggestions for improvement. As such, they are not infallible, and their suggestions should not be accepted blindly. Instead, you should take the time to review the suggestions and consider whether they make sense in the context of your code. You should also consider whether the suggested changes align with your coding style and the overall architecture of your project. 3. Maintain Code Readability While code explainers can help you identify potential issues and inefficiencies in your code, they can't replace the human understanding and intuition that is necessary to write readable and maintainable code. Readability is one of the most critical aspects of a good code. Readable code is easy to understand, debug, and maintain. Therefore, even though code explainers are available in your project, you should still take care to ensure that your code remains readable and understandable. 4. Balance Automation with Critical Thinking Remember, code explainers are simply tools that can provide useful insights and suggestions. They are not a substitute for human intelligence and intuition. It's important to remember that while code explainers can speed up the development process and improve code quality, they should not replace critical thinking. Always question the suggestions provided by the tool and use your judgment to decide whether they make sense in the context of your code. 5. Tailor the Settings to Suit Your Coding Style Most code explainers come with various settings that you can customize to fit your needs better. For example, you can adjust the sensitivity of the tool to control the number of suggestions it provides. Some types of tools allow you to customize the types of suggestions and additions they make to the code or even provide natural language instructions specifying how you want the tool to behave. In conclusion, automating code explainers can be a powerful ally in your software development process. However, to reap the maximum benefits of this technology, it's crucial to follow the best practices discussed in this article. By integrating the tool into your workflow, regularly reviewing and refining its suggestions, maintaining code readability, balancing automation with critical thinking, and tailoring the settings to suit your coding style, you can ensure that you get the most out of your code explainer tool.

Understanding Automated Code Explainers and Best Practices

Despite the evident advantages of automated testing in enhancing speed, precision, and cost-efficiency in specific scenarios, an intriguing statistic reveals that only . 5% of companies commit to fully automated testing In this article, we will unravel why manual testing remains an indispensable component of every project for the foreseeable future despite the relentless advancement of automated testing technologies. Manual Testing Manual testing forms the foundation for automation and is crucial in various scenarios: In situations where product functionality undergoes frequent changes, manual testing becomes pivotal. It offers the flexibility to swiftly adapt to changes without the burden of perpetually updating automated scripts. Rapidly Changing Functionality: Manual testing shines when dealing with intricate or ambiguous test scenarios, such as multi-tier user authentication processes. Manual testers excel in improvisation, navigating the application to uncover edge cases and modifying their test strategies. Complex for Automation or Undefined Test Scenarios: Manual testers provide critical insights into usability, design effectiveness, and overall user satisfaction, elements often missed by automated processes. UI/UX Testing: Automation may not be necessary on smaller projects where a few manual testers suffice. In such contexts, manual testing provides comprehensive coverage and is a more cost-effective and practical approach. Small-Scale Projects: Example: Evaluating a novel gesture-based interaction feature in a mobile app requires exclusively manual testing. This involves assessing the naturalness, responsiveness, and overall user satisfaction with these interactions, which automated tests cannot effectively measure. Testers manually execute gestures to navigate the app, evaluating the feature's intuitiveness and user experience firsthand. Automated Testing Automated testing excels in stable environments and for regression testing. It's ideal for scenarios where functionalities are consistent and manual testing might be limited due to high volume, complexity, or repetitive tasks. Automated tests can run independently, even overnight, providing quick, reliable results by the following day. This makes it a strategic choice for ensuring stability and efficiency in the testing process. Example: Automated testing is essential for stress testing a web application expected to handle high traffic, such as during a flash sale. It's impractical for manual testing to simulate the scale of simultaneous user activities that automated tools like JMeter can, ensuring the application's performance under peak load conditions. Comparative Analysis of Manual vs. Automated Testing Before delving into the central question of our article, let's examine the critical differences between automated and manual testing. This table summarizes the main distinctions between manual and automated testing, offering a quick overview of their strengths and challenges. The choice between them depends on project requirements, such as scale, complexity, and the need for immediate feedback. Most organizations opt for a hybrid approach, combining both methods to ensure comprehensive testing and high-quality software delivery. Why a Balanced Approach to Automated and Manual Testing Is Best for Projects Therefore, the most optimal approach for this project is the combination of manual and automated testing. Here’s why a balanced integration of automated and manual testing methodologies emerges as the best practice for projects: #1. The Base for Automation Manual testing serves as the bedrock upon which automated testing is built. Test cases and scenarios are validated manually to establish a baseline for expected behavior. These manual tests help identify critical features, user workflows, and potential areas of concern, which are then translated into automated test scripts. Without this initial manual testing phase, automating tests becomes a blind process lacking the human insights to detect early defects and vulnerabilities. __Manual __ test case example: Submit a «Contact Us» form with the valid data TITLE: STEPS: Navigate to the «Contact Us» page Enter the valid data Check the «Privacy Policy» checkbox Click the «Send Message» button The «Message is sent» success popup should be displayed EXPECTED RESULT: __Automation __ example (using Playwright and TypeScript): import { test } from '@playwright/test'; import ContactUsPage from '../pages/ContactUsPage'; const validFullName = 'Test Customer'; const validEmail = 'testemail@gmail.com'; const validMessage = 'It is interesting to learn about your services, get advice on my project and discuss the free trial in more detail.\nThank you!\nWaiting for feedback!'; test.describe('Contact Us form test', () => { test('Submit a «Contact Us» form with the valid data', async ({ page }) => { const contactUsPage = new ContactUsPage(page); // Navigate to the «Contact Us» page await contactUsPage.navigate(); // Enter the valid data await contactUsPage.enterFullName(validFullName); await contactUsPage.enterEmail(validEmail); await contactUsPage.enterMessage(validMessage); // Check the «Privacy Policy» checkbox await contactUsPage.clickPrivacyPolicyCheckbox(); // Click the «Send Message» button await contactUsPage.clickSendMessageButton(); // Verify success message is displayed await contactUsPage.isSuccessModalDisplayed(); }); }); #2. Accessibility Testing Ensuring that software is accessible to users with disabilities is a critical aspect of quality assurance. Manual testing allows testers to evaluate the application from the perspective of users with disabilities, including screen readers, keyboard navigation, and other assistive technologies. These aspects are challenging to automate fully, making manual testing essential to ensuring inclusivity and compliance with accessibility standards. consider a government website that provides essential information to citizens. Manual testing ensures that individuals with visual impairments can navigate the site using screen readers, access necessary forms, and receive critical updates. For instance, These functionalities are challenging to automate comprehensively due to the varied needs of users with disabilities, emphasizing the indispensability of manual accessibility testing in guaranteeing equal access to crucial information. #3. Risk Management By leveraging both testing methodologies, teams can ensure a more comprehensive risk assessment. Imagine a healthcare software project where manual testing is crucial for validating patient data input and ensuring the software's compliance with medical regulations. Automated testing complements this by verifying data integrity and the system's overall functionality. #4. Continuous Integration and Agile Environments Combining automated and manual testing in continuous integration (CI) and agile development environments ensures that quality keeps pace with development. For instance, consider a social media platform where automated tests verify core functionalities like user authentication, while manual testing focuses on user interface improvements and usability. #5. Comprehensive Bug Discovery Manual testing can uncover subtle, context-specific bugs that automated scripts may overlook. Testers can apply human intuition and creativity to explore the software from end-users perspectives, identifying issues that might not be covered in automated test cases. This aspect becomes crucial for ensuring a polished user experience, catching unexpected edge cases, and improving overall software quality. imagine testing an e-commerce website where an automated script checks the functionality of the "Add to Cart" button and appears to work flawlessly. However, during manual testing, a tester notices that when adding multiple items rapidly, the cart icon briefly flashes, potentially confusing users about the status of their selections. For instance, This nuanced issue, uncovered by human intuition and creativity, highlights the importance of manual testing in ensuring a user-friendly experience, improving overall software quality, and benefiting end-users. Сonclusions In conclusion, the ideal approach to software testing lies in a balanced integration of manual and automated testing methodologies. Manual testing, with its adaptability, user-centric insights, and ability to uncover nuanced issues, is the foundation upon which automation is built. Combining both approaches ensures comprehensive testing, high-quality software, and an improved user experience, making it the best practice for modern projects.

To Automate or Not: Navigating Manual and Automated Testing Paradigms

স্বয়ংক্রিয় বা না করা: ম্যানুয়াল এবং স্বয়ংক্রিয় পরীক্ষার দৃষ্টান্ত নেভিগেট করা

Automatisieren oder nicht: Navigieren in manuellen und automatisierten Testparadigmen

Automatizar o no: navegar por paradigmas de pruebas manuales y automatizadas

Automatiser ou non : naviguer dans les paradigmes de tests manuels et automatisés

स्वचालित करें या नहीं: मैनुअल और स्वचालित परीक्षण प्रतिमानों को नेविगेट करना

自動化するか否か: 手動テスト パラダイムと自動テスト パラダイムをナビゲートする

자동화 여부: 수동 및 자동 테스트 패러다임 탐색

Para Automatizar ou Não: Navegando nos Paradigmas de Testes Manuais e Automatizados

Автоматизировать или нет: ориентироваться в парадигмах ручного и автоматического тестирования

Otomatikleştirmek veya Otomatikleştirmemek: Manuel ve Otomatik Test Paradigmalarında Gezinme

Để tự động hóa hay không: Điều hướng các mô hình thử nghiệm thủ công và tự động

是否自动化：导航手动和自动测试范式

Building a DevOps Automated Deployment Pipeline using Flask, Aptible, CircleCI, and Prometheus

Authors: (1) Nowshin Nawar Arony; (2) Ze Shi Li; (3) Bowen Xu; (4) Daniela Damian. Table of Links Abstract & Introduction Motivation Related Work Methodology A Taxonomy of Inclusiveness Inclusiveness Concerns in Different Types of Apps Inclusiveness Across Different Sources of User Feedback Automated Identification of Inclusiveness User Feedback Discussion Conclusion & References 8 AUTOMATED IDENTIFICATION OF INCLUSIVENESS USER FEEDBACK In answering our fourth research question, How effective are pre-trained large language models in automatically identifying inclusiveness-related user feedback?, we assessed the effectiveness of the five LLMs, as detailed in Section 4.3 through evaluating the same dataset. We fine-tuned the pre-trained models on each of the sources and measured the performance in terms of precision, recall, F1-score, and accuracy. The performance results of the five models for each source are outlined in Table 3. We found that the overall evaluation results are best for Twitter user feedback. All the evaluation metrics are above 0.85 for Twitter for all five different classifiers. Among the five classifiers, we find BART to have the best results in terms of F1-score, achieving a value of 0.930. One possible reason for the better performance on Twitter may be that Twitter data has a lot of unrelated discussion (e.g. ads), making it relatively easier for the classifier to differentiate between inclusiveness and non-inclusiveness. Since there are many Twitter posts that consist of completely random dialogue or topics obviously unrelated to inclusiveness, this may help the models to classify the user feedback. We observe that the classification results for app reviews are not as good as Twitter’s (i.e., roughly 8-12% lower). For Play Store, the best performing classifier is BERT, with an F1-score of 0.849. There are many user feedback reviews that report bugs concerning the apps, but not all of these bug reports are about inclusiveness. This may be a reason for the increased difficulty for the classifiers to identify inclusiveness in app reviews. Finally, we observed the classifiers exhibiting a comparatively lower performance on Reddit in comparison to the other feedback sources. The best performing classifier for Reddit data is GPT-2, whereas the overall performance for classifiers for Reddit is roughly 8-14% lower than Twitter. The most likely reason why performance on Reddit is lower is that user feedback from Reddit is often complex and detailed in nature, which may contain an assortment of topic discussions. Identifying the inclusiveness aspect in a Reddit post is less clear cut than in a Twitter post or app review. This paper is under CC 4.0 license. available on arxiv

Automated Identification of Inclusiveness User Feedback: Testing the Effectiveness of 5 LLMs

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