Mining Social Media Data for Sentiment Analysis and Trend Prediction

Authors

  • Savitha R  Lecturer, Department of Information Science and Engineering, Government Polytechnic, Kalaburagi, Karnataka, India
  • Smithu B S  Lecturer, Department of Computer Science and Engineering, Government Polytechnic, Channasandra, Bengaluru, Karnataka, India

DOI:

https://doi.org/10.32628/IJSRST2251414

Keywords:

Customer churn, machine learning, decision trees, random forests, support vector machines, predictive modelling, telecom industry, retention strategies, churn prediction, feature selection.

Abstract

This research investigates the use of machine learning techniques in predicting customer churn within the telecom industry. The primary objective is to develop a predictive model capable of identifying high-risk customers, allowing companies to implement targeted retention strategies. Various machine learning algorithms, including decision trees, random forests, and support vector machines, were evaluated for their accuracy in predicting churn. The methodology involved pre-processing the dataset, feature selection, model training, and evaluation using metrics such as precision, recall, and F1-score. Key findings suggest that the random forest algorithm outperforms others in terms of predictive accuracy, providing significant improvements over traditional models. The results highlight the potential of machine learning in customer retention and churn prediction, offering telecom companies a strategic tool to enhance customer loyalty and reduce churn rates.

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International Journal of Scientific Research in Science and Technology

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Published

2022-06-30

Issue

Volume 9, Issue 3, May-June-2022

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Savitha R, Smithu B S "Mining Social Media Data for Sentiment Analysis and Trend Prediction" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 3, pp.624-636, May-June-2022. Available at doi : https://doi.org/10.32628/IJSRST2251414