Abstract Of The Project :
Objective: This project aims to analyze sentiment in e-commerce product reviews using machine learning techniques, focusing on classification and pairwise review ranking to determine relevance.
Methodology: Utilizing Python's Pandas and Scikit-learn libraries, the project preprocesses and filters a dataset of product ratings and reviews. It employs TF-IDF vectorization and logistic regression for classification, and cosine similarity for pairwise review scoring.
Results: The project generates insights through visualizations such as cross-tabulations, heatmaps for classification reports, and pie charts illustrating the distribution of informative and non-informative reviews, offering a comprehensive understanding of product sentiment.
Significance: By providing a systematic approach to sentiment analysis, this project equips e-commerce platforms with valuable tools for evaluating customer feedback, enhancing product relevance, and improving overall customer satisfaction and retention.
Workflow :
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Data Collection: Gather product reviews data from e-commerce platforms.
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Data Preprocessing and Filtering: Clean and preprocess the data, removing any irrelevant information or noise.
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Feature Extraction (TF-IDF Vectorization): Convert text data into numerical feature vectors using TF-IDF vectorization.
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Model Training (Logistic Regression): Train a logistic regression model to classify reviews as informative or non-informative based on their sentiment.
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Model Evaluation (Classification Report): Evaluate the performance of the trained model using a classification report, which includes metrics such as precision, recall, and F1-score.
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Pairwise Review Scoring: Calculate pairwise review scores using cosine similarity to rank reviews for each product.
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Visualization and Interpretation: Visualize the results through various charts and graphs, such as cross-tabulations, heatmaps, and pie charts, to interpret the findings and gain insights into product sentiment and relevance.