Scholarlink

<p>In the contemporary era, individuals utilize social media platforms such as Twitter, Facebook, and Instagram to disseminate their opinions and ideas to a broad audience. Twitter stands out as a platform with a substantial user base that generates millions of tweets daily. Notably, Indian Railways, overseeing the second-largest railway system globally, actively encourages passenger interaction through tweets on Twitter. However, the sheer volume of data generated poses a challenge for the Railway Administration to respond comprehensively to every tweet, including urgent matters and inquiries requiring immediate attention. This research proposes a solution in the form of a website that categorizes live, real-time tweets into various classifications, including maintenance, medical issues, security concerns, delays, and others, ensuring simultaneous updates. The method employs advanced technologies such as big data, Natural Language Processing (NLP), Machine Learning, and Deep Learning techniques. The proposed system achieved a peak accuracy of 81% using the BERT (Bidirectional Encoder Representations from Transformers) model, surpassing SVM (Support Vector Machine) with TF-IDF (Term Frequency-Inverse Document Frequency), which attained an accuracy of 78%. This innovative approach facilitates streamlined responses by the Railway administration to each tweet through the dedicated website, providing insights into the quality of service based on passenger feedback.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Technological Framework and Analytical Approach in Developing a real-time twitter-integrated System for Rail Transit Grievance Management

  • 期刊论文

  • 发表日期：

    2025年8月18日

查看全文
  • K. Saranya,
  • A. Saran Kumar

摘要

In the contemporary era, individuals utilize social media platforms such as Twitter, Facebook, and Instagram to disseminate their opinions and ideas to a broad audience. Twitter stands out as a platform with a substantial user base that generates millions of tweets daily. Notably, Indian Railways, overseeing the second-largest railway system globally, actively encourages passenger interaction through tweets on Twitter. However, the sheer volume of data generated poses a challenge for the Railway Administration to respond comprehensively to every tweet, including urgent matters and inquiries requiring immediate attention. This research proposes a solution in the form of a website that categorizes live, real-time tweets into various classifications, including maintenance, medical issues, security concerns, delays, and others, ensuring simultaneous updates. The method employs advanced technologies such as big data, Natural Language Processing (NLP), Machine Learning, and Deep Learning techniques. The proposed system achieved a peak accuracy of 81% using the BERT (Bidirectional Encoder Representations from Transformers) model, surpassing SVM (Support Vector Machine) with TF-IDF (Term Frequency-Inverse Document Frequency), which attained an accuracy of 78%. This innovative approach facilitates streamlined responses by the Railway administration to each tweet through the dedicated website, providing insights into the quality of service based on passenger feedback.