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Data Interview Question

Sentiment Evaluation on Customer Reviews

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Requirements Clarification & Assessment

  1. Objective Clarification:

    • Sentiment Analysis Scope: Determine whether the task is to classify reviews as positive, negative, or neutral, or to predict future feedback sentiment based on historical data.
    • Output Format: Confirm the desired output format, such as a sentiment score or categorical labels (e.g., Positive, Negative, Neutral).
  2. Dataset Understanding:

    • Dataset Composition: Understand the size, structure, and variety of the dataset, including the number of reviews and the presence of any metadata (e.g., product categories).
    • Data Quality: Assess the quality of the dataset, identifying missing values, duplicates, or anomalies that could affect model performance.
  3. Preprocessing Needs:

    • Text Cleaning: Identify necessary preprocessing steps such as removing stop words, punctuation, and special characters, and converting text to lowercase.
    • Feature Engineering: Explore the need for advanced techniques like n-grams, lemmatization, and stemming to enhance the quality of the input data.
  4. Model Requirements:

    • Algorithm Selection: Determine which machine learning or deep learning algorithms are suitable for the task, considering the dataset size and complexity.
    • Performance Metrics: Define success metrics such as accuracy, precision, recall, and F1-score for evaluating model performance.