Over the last two decades, social media has become an integral part of people’s daily lives, serving as a crucial tool for gathering information, tracking trends, and exploring collective feelings. In this research paper, the power of Machine Learning (ML) models was harnessed to conduct an extensive analysis of sentiments expressed in Twitter data. By analyzing tweets, people can access a wealth of sentiment-related information, which in turn enhances their understanding of public opinions on a wide range of social media topics and issues. The main objective was to determine whether tweets convey negative, positive, or neutral emotions, a process commonly known as sentiment analysis. This analytical approach allows for the identification and categorization of emotions conveyed in text, providing valuable insights into sentiments. In this research paper, Sentiment Analysis is conducted using the MATLAB statistical and machine learning toolbox. The analysis focuses on calculating and visually illustrating both positive and negative sentiments, over time for various airlines. The study utilizes three distinct classifiers: Decision Tree, K-Nearest Neighbors (KNN), along with efficient Logistic Regression and efficient Linear Support Vector Machine (SVM) techniques. These models underwent training utilizing Twitter data pertaining to the aviation industry, and their performance was assessed through a 10-fold cross-validation process. The outcome revealed a maximum accuracy of 80.1%. This sophisticated sentiment analysis and loyalty projection framework transcends its initial aviation focus, providing businesses across various sectors with the capability to augment customer retention and cultivate brand loyalty.
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11 October 2024
1ST INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGY AND SUSTAINABLE SOLUTIONS
24–25 November 2023
Punjab, India
Research Article|
October 11 2024
Airline consumer sentimental analysis of social tweets using Machine Learning models
Rashpinder Kaur;
Rashpinder Kaur
a)
Department of Electronics and Communication Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University
, Rajpura, Punjab, India
a)Corresponding author: [email protected]
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Isha Gupta;
Isha Gupta
b)
Department of Electronics and Communication Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University
, Rajpura, Punjab, India
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Sonam Aggarwal;
Sonam Aggarwal
c)
Department of Electronics and Communication Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University
, Rajpura, Punjab, India
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Harsh Kumar;
Harsh Kumar
d)
Department of Electronics and Communication Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University
, Rajpura, Punjab, India
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Venushree Kahol;
Venushree Kahol
e)
Department of Electronics and Communication Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University
, Rajpura, Punjab, India
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Pavleen Singh
Pavleen Singh
f)
Department of Electronics and Communication Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University
, Rajpura, Punjab, India
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a)Corresponding author: [email protected]
AIP Conf. Proc. 3209, 060008 (2024)
Citation
Rashpinder Kaur, Isha Gupta, Sonam Aggarwal, Harsh Kumar, Venushree Kahol, Pavleen Singh; Airline consumer sentimental analysis of social tweets using Machine Learning models. AIP Conf. Proc. 11 October 2024; 3209 (1): 060008. https://doi.org/10.1063/5.0228495
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