Abstract: 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 two-fold: 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.

Peter Adebowale Olujimi and Abejide Ade-Ibijola, “Synthesis of Comments to Social Media Posts for Business Applications” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131298

@article{Olujimi2022,
title = {Synthesis of Comments to Social Media Posts for Business Applications},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131298},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131298},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Peter Adebowale Olujimi and Abejide Ade-Ibijola}
}