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

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%.