Could you explain your experience and approach to text preprocessing and tokenization in Natural Language Processing (NLP)?

How To Approach: Associate

  1. Describe your work related to NLP, text preprocessing, and tokenization.
  2. Discuss practical challenges and how you overcame them.
  3. Mention the specific tools, libraries, or algorithms used.
  4. Share the outcome of your work, emphasizing on business benefits.

Sample Response: Associate

In my role as a Data Analyst at BigDataSolutions, I have worked extensively on NLP projects. One of the projects I am most proud of involved building a customer review analysis system.

The primary challenge in this project was the eclectic mix of customer review data, which was noisy and unstructured. To clean and structure the data, I implemented a systematic text preprocessing routine that involved techniques like removal of punctuation, lowercasing, stop-words removal, and stemming, using libraries like NLTK and spaCy in Python.

For tokenization, I used the WhitespaceTokenizer from the NLTK library. This tool helped us separate text into individual words, essential for our subsequent analyses. Leveraging these tools and techniques, we could accurately categorize customer sentiment, positively influencing our marketing strategies and product improvements.

More Interviews
transformer models nlp taskslong text generation coherenceusing scikit learn ml modelsmatplotlib seaborn visualization ml data