Factors Affecting Customer Satisfaction with The Telecommunication Industry in Saudi Arabia
Bibliographic record
Abstract
Telecommunications is a customer-oriented industry in which client satisfaction is crucial for an organization's survival. Social media plays a vital role in customer decisions, acting as both a search tool and a communication channel. On social media platforms, customers can air their grievances, and a company can use these complaints to improve its products and services. During the first quarter of 2022, sentiment analysis was conducted to evaluate customer satisfaction with telecom services in Saudi Arabia. With a machine-learning approach, more than 90K comments were recorded and categorized as positive, negative, or neutral. For the classification, we utilised a support vector machine (SVM) model with an average accuracy of 88%. After that, We utilised thematic analysis of social engagement opinions. We identified seven themes among the comments related to factors affecting efficiency and satisfaction with telecommunications services: product, package, price, promotion, place, people, and public relations. In conclusion, we recommend some solutions to improve efficiency and increase customer satisfaction in the telecom sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".