Skip to main content

Skills application solution

Worked example
In Progress

Skills application solution

Use this expert example to compare how you selected text features, explained the process and translated your findings into practical recommendations.

Skills solution illustration

Trustpilot report: Understanding and avoiding 1-star reviews

Key terms linked with 1-star reviews

  • “number there” suggested frustration around accessibility or the inability to reach support.
  • “line day” pointed to long waits, queues and poor service efficiency.
  • “that person” indicated negative interactions with an individual staff member or representative.
  • “weekend call” highlighted out-of-hours support gaps and poor weekend responsiveness.

How the team obtained these insights

  • Text preparation: Reviews were collected and lemmatised so related word forms were standardised.
  • Vectorisation: A CountVectorizer with an ngram_range of (1, 2) captured both single words and two-word phrases.
  • Filtering: A max_df threshold of 0.8 removed overly common, low-value terms.
  • Model training: Logistic regression was trained to predict whether a review was 1-star.
  • Evaluation: The team compared the model against a baseline and then used the learned coefficients to identify the most predictive terms.
Reported performance:
  • Baseline accuracy: 0.696
  • Training accuracy: 0.99
  • Test accuracy: 0.93
Lightbulb icon

Why this worked

The combination of lemmatisation, n-grams and an interpretable linear model made it possible to trace poor review outcomes back to specific phrases, not just overall sentiment.

Recommendations for reducing 1-star reviews

  • Improve accessibility: Make contact routes obvious and provide multiple support channels.
  • Reduce waiting time: Streamline service processes, improve queue handling and communicate delays clearly.
  • Strengthen customer interactions: Train staff in empathy, product knowledge and issue resolution.
  • Extend out-of-hours support: Improve weekend support coverage and self-service resources.
Pause icon

Tips for applying this skill in your role

  • Experiment with multiple NLP representations because the best choice depends on the task.
  • Balance richer transformations with the extra compute they require.
  • If a deployed language model depends on a fixed vocabulary, refresh it periodically so it keeps up with new phrases and behaviours.