A Machine Learning Approach to Detect Customer Satisfaction From Multiple Tweet Parameters
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.
- Jurafsky, D. and Martin, J.H. (2009) “N-gram Language Models,” in Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Upper Saddle River, NJ: Pearson Prentice Hall.
- P. Soujanya, E. Cambria and A. Gelbukh. ”Aspect extraction for opinion mining with a deep convolutional neural network.”
- C. Baydogan and B. Alatas, ”Detection of Customer Satisfaction on Unbalanced and Multi-Class Data Using Machine Learning Algorithms,”
- Sabbeh, Sahar F. ”Machine-learning techniques for customer retention: A comparative study.”
- Twitter US Airline Sentiment. [online] Available at: ¡http://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment¿ [Accessed 30 September 2022].
- Wang, W.Y. and Yang, D. (2015) “That’s so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using #petpeeve tweets,”
- Niu, T. and Bansal, M. (2018) “Adversarial over-sensitivity and over-stability strategies for dialogue models,”
- Sennrich, R., Haddow, B. and Birch, A. (2016) “Improving neural machine translation models with monolingual data,”
- Jia, R. and Liang, P. (2017) “Adversarial examples for evaluating reading comprehension systems,”
- Pennington, J., Socher, R. and Manning, C. (2014) “Glove: Global vectors for word representation,”
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.