The demand for automated customer support approaches in customer-centric environments has increas... more The demand for automated customer support approaches in customer-centric environments has increased significantly in the past few years. Natural Language Processing (NLP) advancement has enabled conversational AI to comprehend human language and respond to enquiries from customers automatically independent of the intervention of humans. Customers can now access prompt responses from NLP chatbots without interacting with human agents. This application has been implemented in numerous business sectors, including banking, manufacturing, education, law, and healthcare, among others. This study reviewed earlier studies on automating customer queries using NLP approaches. Using a systematic review methodology, 73 articles were analysed from reputable digital resources. The evaluated result offers an in-depth review of prior studies investigating the use of NLP techniques for automated customer service responses, including details on existing studies, benefits, and potential future study t...
International Journal of Advanced Computer Science and Applications
Responding to enormous comments on social media platforms is one major challenge facing businesse... more Responding to enormous comments on social media platforms is one major challenge facing businesses in recent times, especially when dealing with irate consumers. Customers have increasingly adopted social networks as a platform for expressing their concerns and posting comments on business pages, posing a great challenge for customer support agents and digital marketers alike. Analyzing and responding manually to these enormous comments is a time-consuming task, necessitating the adoption of Artificial Intelligence (AI) tool that can complete the task swiftly automatic comprehension of social media posts for comment generation. In this paper, we present algorithms and a tool for the automatic comprehension of customer tweets and generation of responses to these tweets. This was done in twofold: using existing Natural Language Processing (NLP) libraries to preprocess and tokenize these tweets, and secondly, using rule-based algorithms to find a matching response to each customer, based on the array of extracted tokens from the customer's tweet. This was built into a tool called Comment-Synthesizer. This tool takes unfiltered tweets as input, preprocesses the tweets, and matches the tweet with predefined responses using a rule-based algorithm with a success rate of 76%. This tool, if implemented in a desktop automation application, can be used to respond automatically to a large volume of customers' social media comments/posts.
The demand for automated customer support approaches in customer-centric environments has increas... more The demand for automated customer support approaches in customer-centric environments has increased significantly in the past few years. Natural Language Processing (NLP) advancement has enabled conversational AI to comprehend human language and respond to enquiries from customers automatically independent of the intervention of humans. Customers can now access prompt responses from NLP chatbots without interacting with human agents. This application has been implemented in numerous business sectors, including banking, manufacturing, education, law, and healthcare, among others. This study reviewed earlier studies on automating customer queries using NLP approaches. Using a systematic review methodology, 73 articles were analysed from reputable digital resources. The evaluated result offers an in-depth review of prior studies investigating the use of NLP techniques for automated customer service responses, including details on existing studies, benefits, and potential future study t...
International Journal of Advanced Computer Science and Applications
Responding to enormous comments on social media platforms is one major challenge facing businesse... more Responding to enormous comments on social media platforms is one major challenge facing businesses in recent times, especially when dealing with irate consumers. Customers have increasingly adopted social networks as a platform for expressing their concerns and posting comments on business pages, posing a great challenge for customer support agents and digital marketers alike. Analyzing and responding manually to these enormous comments is a time-consuming task, necessitating the adoption of Artificial Intelligence (AI) tool that can complete the task swiftly automatic comprehension of social media posts for comment generation. In this paper, we present algorithms and a tool for the automatic comprehension of customer tweets and generation of responses to these tweets. This was done in twofold: using existing Natural Language Processing (NLP) libraries to preprocess and tokenize these tweets, and secondly, using rule-based algorithms to find a matching response to each customer, based on the array of extracted tokens from the customer's tweet. This was built into a tool called Comment-Synthesizer. This tool takes unfiltered tweets as input, preprocesses the tweets, and matches the tweet with predefined responses using a rule-based algorithm with a success rate of 76%. This tool, if implemented in a desktop automation application, can be used to respond automatically to a large volume of customers' social media comments/posts.
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Papers by Peter Olujimi