Sentiment Evaluation on Customer Reviews

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.