Research Article Open Access

Analyzing and Automating Customer Service Queries on Twitter Using Robotic Process Automation

T. N. Ram Kumar1, Ganeshayya Shidaganti1, Prarthana Anand1, Shreya Singh1 and Sreya Salil1
  • 1 Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology, Bengaluru, India

Abstract

Several product companies have turned to social media to analyze customer satisfaction and provide customer service to disgruntled customers. Product twitter handles are flooded with tweets every day. Customer service professionals struggle to find and resolve complaints from numerous tweets, which results in high wait times for a response, huge costs for the company, and frustrated customers. Automating customer services involves the use of Artificial Intelligence (AI) and Natural Language Processing (NLP) to emulate customer service offered by professionals. Pre-processing and deriving insights from real data are difficult and several start-ups cannot afford funds to maintain a data analysis team. Existing technologies for chatbots and analysis can be improved with new machine learning models, training existing models more and ensuring that responses generated resemble human interactions. BERT-CNN-BiLSTM modules were integrated into a model for sentiment analysis on scraped tweets from twitter using TWINT that achieved an accuracy of 96%. For the labeling and categorization of tweets, the logistic regression linear model achieved the highest accuracy of 97% compared to the other 3 classification models. For the automated chatbot, a model trained with BERT and open AI-GPT achieved an accuracy of 78%.

Journal of Computer Science
Volume 19 No. 4, 2023, 514-525

DOI: https://doi.org/10.3844/jcssp.2023.514.525

Submitted On: 30 November 2022 Published On: 24 March 2023

How to Cite: Kumar, T. N. R., Shidaganti, G., Anand, P., Singh, S. & Salil, S. (2023). Analyzing and Automating Customer Service Queries on Twitter Using Robotic Process Automation. Journal of Computer Science, 19(4), 514-525. https://doi.org/10.3844/jcssp.2023.514.525

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Keywords

  • Automation
  • Robotic Process Automation
  • Data Analytics
  • UiPath
  • Customer Service
  • TWINT