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Customer Churn Prediction Based on Sentiment Score
In recent years, the telecommunications industry has witnessed intensified competition, wherein the expense associated with acquiring new consumers exceeds that of sustaining existing ones. Consequently, predicting customer churn prior to its occurrence has become essential. This study proposes a se...
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| Format: | Thesis |
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AUC Knowledge Fountain
2024
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| _version_ | 1867613423971336192 |
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| access_status_str | Open Access |
| author | Al-Safi, Shadha |
| author_browse | Al-Safi, Shadha |
| author_facet | Al-Safi, Shadha |
| author_sort | Al-Safi, Shadha |
| collection | Thesis |
| description | In recent years, the telecommunications industry has witnessed intensified competition, wherein the expense associated with acquiring new consumers exceeds that of sustaining existing ones. Consequently, predicting customer churn prior to its occurrence has become essential. This study proposes a sentiment-based customer churn prediction model in which the sentiment of customers is predicted using Random Forest. Subsequently, the derived sentiment predictions are combined with additional features to predict customer churn. The ensemble technique is applied to predict churn, consisting of K-nearest neighbors, Support Vector Machines, Random Forest as base learners, and Multiple Layer Perceptron as a meta learner. Moreover, mutual information is applied to select the pertinent features impacting customer churn, and the class imbalance is handled through the utilization of the class weighted technique. The results of the experiments reveal that the proposed model surpassed the state-of-the-art customer churn models as it achieved an accuracy of 98.86%, an AUC of 99.47 %, and an F1-score of 97.77%. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-3347 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:55.364Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-3347 Customer Churn Prediction Based on Sentiment Score Al-Safi, Shadha In recent years, the telecommunications industry has witnessed intensified competition, wherein the expense associated with acquiring new consumers exceeds that of sustaining existing ones. Consequently, predicting customer churn prior to its occurrence has become essential. This study proposes a sentiment-based customer churn prediction model in which the sentiment of customers is predicted using Random Forest. Subsequently, the derived sentiment predictions are combined with additional features to predict customer churn. The ensemble technique is applied to predict churn, consisting of K-nearest neighbors, Support Vector Machines, Random Forest as base learners, and Multiple Layer Perceptron as a meta learner. Moreover, mutual information is applied to select the pertinent features impacting customer churn, and the class imbalance is handled through the utilization of the class weighted technique. The results of the experiments reveal that the proposed model surpassed the state-of-the-art customer churn models as it achieved an accuracy of 98.86%, an AUC of 99.47 %, and an F1-score of 97.77%. 2024-02-08T08:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/2306 https://fount.aucegypt.edu/context/etds/article/3347/viewcontent/shadha_hamed_al_safi_thesis.pdf https://fount.aucegypt.edu/context/etds/article/3347/filename/2/type/additional/viewcontent/Shadha_approval.pdf Theses and Dissertations AUC Knowledge Fountain Churn prediction - Sentiment score - Ensemble - Class weighted Other Computer Engineering |
| spellingShingle | Churn prediction - Sentiment score - Ensemble - Class weighted Other Computer Engineering Al-Safi, Shadha Customer Churn Prediction Based on Sentiment Score |
| title | Customer Churn Prediction Based on Sentiment Score |
| title_full | Customer Churn Prediction Based on Sentiment Score |
| title_fullStr | Customer Churn Prediction Based on Sentiment Score |
| title_full_unstemmed | Customer Churn Prediction Based on Sentiment Score |
| title_short | Customer Churn Prediction Based on Sentiment Score |
| title_sort | customer churn prediction based on sentiment score |
| topic | Churn prediction - Sentiment score - Ensemble - Class weighted Other Computer Engineering |
| url | https://fount.aucegypt.edu/etds/2306 https://fount.aucegypt.edu/context/etds/article/3347/viewcontent/shadha_hamed_al_safi_thesis.pdf https://fount.aucegypt.edu/context/etds/article/3347/filename/2/type/additional/viewcontent/Shadha_approval.pdf |
| work_keys_str_mv | AT alsafishadha customerchurnpredictionbasedonsentimentscore |