Claro Colombia Customer Experience Patterns: Sentiment Analysis
Bibliographic record
Abstract
During the second quarter of 2024, Claro Colombia positioned itself as the market leader in terms of user numbers, with more than 589,000 new ports. However, this growth has not been matched by an improvement in service perception. On social media, users continue to report high levels of dissatisfaction, with frequent complaints about service quality, customer service, and network stability. Today more than ever, social media is a direct reflection of the customer experience. Users not only share their experiences but also shape the opinions of others. Ignoring these voices can mean losing the public's trust; however, listening to them and analyzing them strategically can make a difference in customer relationships. This work seeks precisely that: to understand what Claro Colombia users feel and think through the analysis of their comments on platforms such as Facebook, Threads and X. Applying artificial intelligence and sentiment analysis techniques, patterns and trends will be identified that allow generating useful information to make decisions aimed at improving the service, strengthening the brand's reputation and, above all, better connecting with the real needs of users. During the second quarter of 2024, Claro Colombia positioned itself as the market leader in terms of the number of users, with more than 589,000 new portings. However, this growth has not been accompanied by an improvement in the perception of the service. On social media, users continue to report high levels of dissatisfaction, with frequent complaints about the quality of service, customer service, and network stability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".