In the realm of E-Commerce, customer reviews serve as vital resources for making informed purchase decisions. However, the sheer volume of reviews often overwhelms customers, making it challenging to discern between valuable and irrelevant feedback.
Develop a model that employs pairwise ranking to prioritize product reviews, emphasizing the most pertinent ones while downgrading less relevant or irrelevant reviews.
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Data Preprocessing:
- Language Detection: Identify the language of each review.
- Gibberish Detection: Detect and filter out reviews with incoherent or nonsensical content.
- Profanity Detection: Identify and manage reviews with inappropriate language.
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Feature Extraction:
- Extract meaningful features from reviews to quantify their characteristics.
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Pairwise Review Ranking:
- Implement a pairwise ranking approach to compare and rank reviews based on their relevance to the product.
- Prioritize reviews offering the most valuable insights for potential buyers.
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Classification:
- Relevance Classification: Classify reviews into relevant and irrelevant categories.
- Generate a ranked list of reviews for a specific product, with the most relevant reviews positioned at the top.