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<p>Twitter, a popular social media, and networking tool has evolved into an important source of real-time news and a hub for public opinions. Using Twitter data, the given paper investigates the possibility of applying sentiment analysis to identify tweets as positive, negative, or neutral and then presenting the results visually, resulting in an in-depth analysis of public opinion. To do this, a dynamic opinion dashboard is designed comprising three modules viz. the user interface built on HTML, the backend application running in Python and Twitter, and the data source. The dashboard is built on a Python Flask app that interacts with a Twitter API to collect tweets, a sentiment analysis library to classify the tweets, and a variety of visualization libraries to create charts and graphs. The proposed dashboard provides users with real-time insights from text data in a user-friendly way. The proposed work is validated and compared with existing studies as well as pre-trained sentiment analysis models based on evaluation metrics such as accuracy, precision, recall and F1 score.</p>

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Building a dynamic opinion dashboard to categorize tweets for real-time sentiment analysis

  • 期刊论文

  • 发表日期：

    2025年3月27日

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  • Temitope Adekoya-Cole,
  • Sandra Fernando,
  • Abhilash Maroju,
  • Chandra Kanta Samal,
  • Saurabh Aggarwal,
  • Biswajit Brahma

摘要

Twitter, a popular social media, and networking tool has evolved into an important source of real-time news and a hub for public opinions. Using Twitter data, the given paper investigates the possibility of applying sentiment analysis to identify tweets as positive, negative, or neutral and then presenting the results visually, resulting in an in-depth analysis of public opinion. To do this, a dynamic opinion dashboard is designed comprising three modules viz. the user interface built on HTML, the backend application running in Python and Twitter, and the data source. The dashboard is built on a Python Flask app that interacts with a Twitter API to collect tweets, a sentiment analysis library to classify the tweets, and a variety of visualization libraries to create charts and graphs. The proposed dashboard provides users with real-time insights from text data in a user-friendly way. The proposed work is validated and compared with existing studies as well as pre-trained sentiment analysis models based on evaluation metrics such as accuracy, precision, recall and F1 score.