Summary of A Machine Learning Approach to Detect Customer Satisfaction From Multiple Tweet Parameters, by Md Mahmudul Hasan et al.
A Machine Learning Approach to Detect Customer Satisfaction From Multiple Tweet Parameters
by Md Mahmudul Hasan, Shaikh Anowarul Fattah
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an innovative approach to analyzing customer satisfaction on Twitter by leveraging machine learning techniques to extract insights from flight reviews. The authors recognize the importance of online feedback, particularly on Twitter, where customers frequently share their experiences with airlines. A positive review can boost a company’s growth, while a negative one can significantly impact its revenue and reputation. To address this challenge, the researchers develop a machine learning-based solution that goes beyond traditional sentiment analysis by incorporating additional features such as time, location, username, airline name, and more. The study demonstrates that considering these factors yields better outcomes for machine learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence to help airlines understand what customers like or dislike about their flights. On Twitter, people often share their opinions about flying experiences, which is important because a happy customer can bring in more business, while an unhappy one can hurt the airline’s reputation. To solve this problem, researchers are working on machines that can read and analyze these tweets to figure out what customers like or dislike. Some machines have already been built for this purpose, but they only look at how positive or negative the text is. The authors of this paper took it a step further by including other important details from the tweet, like when and where it was posted, who wrote it, and which airline it’s about. This new approach led to better results. |
Keywords
* Artificial intelligence * Machine learning
