A Machine Learning Approach to Detect Customer Satisfaction From Multiple Tweet Parameters

• URL: http://arxiv.org/abs/2402.15992v1
• Date: Sun, 25 Feb 2024 05:16:43 GMT
• Title: A Machine Learning Approach to Detect Customer Satisfaction From Multiple Tweet Parameters
• Authors: Md Mahmudul Hasan, Dr. Shaikh Anowarul Fattah
• Abstract summary: A positive review can help the company grow, while a negative one can quickly ruin its revenue and reputation. But studying thousands of tweets and analyzing them to find the satisfaction of the customer is quite a difficult task. Some work has already been done on this strategy to automate the procedure using machine learning and deep learning techniques. This work has broadened its perspective to include these qualities.
• Score: 0.8585882243614277
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Since internet technologies have advanced, one of the primary factors in company development is customer happiness. Online platforms have become prominent places for sharing reviews. Twitter is one of these platforms where customers frequently post their thoughts. Reviews of flights on these platforms have become a concern for the airline business. A positive review can help the company grow, while a negative one can quickly ruin its revenue and reputation. So it's vital for airline businesses to examine the feedback and experiences of their customers and enhance their services to remain competitive. But studying thousands of tweets and analyzing them to find the satisfaction of the customer is quite a difficult task. This tedious process can be made easier by using a machine learning approach to analyze tweets to determine client satisfaction levels. Some work has already been done on this strategy to automate the procedure using machine learning and deep learning techniques. However, they are all purely concerned with assessing the text's sentiment. In addition to the text, the tweet also includes the time, location, username, airline name, and so on. This additional information can be crucial for improving the model's outcome. To provide a machine learning based solution, this work has broadened its perspective to include these qualities. And it has come as no surprise that the additional features beyond text sentiment analysis produce better outcomes in machine learning based models.

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