Product Recommendation Sentimental Analysis Streamlit in Python Projects

Product Recommendation Sentimental Analysis Streamlit in Python Projects
Abstract
The Product Recommendation Sentimental Analysis Streamlit Project is an intelligent recommendation system developed using Python that enhances product suggestions by analyzing customer reviews and sentiments. Unlike traditional recommendation systems that rely only on product similarity or user history, this system integrates Sentiment Analysis to understand customer opinions and emotions associated with products. Using Natural Language Processing (NLP) techniques, the system extracts polarity from customer reviews and uses it to refine product rankings. The recommendation engine applies content-based filtering or collaborative filtering, while sentiment scores boost product relevance dynamically. The complete system is deployed as an interactive web application using Streamlit, allowing users to search products, view recommended items, and analyze review sentiments in real time. This project uses Python libraries such as NLTK, Scikit-learn, Pandas, NumPy, and Streamlit, making it a practical solution for personalized product suggestions in e-commerce platforms.
Existing System
Traditional recommendation systems found in e-commerce websites depend primarily on user purchase history, product popularity, or rating-based algorithms. These approaches often fail to capture true customer satisfaction because ratings alone do not reflect detailed user experiences or product quality. Many existing systems lack emotional intelligence and ignore useful insights hidden in customer reviews. For example, a product may have high ratings but many negative comments regarding durability or performance. Such limitations result in poor recommendation accuracy and reduced user trust. Moreover, many systems do not provide an interactive interface for users to explore and understand why specific products were recommended.

Proposed System
The proposed system enhances product recommendations by incorporating sentiment analysis of customer reviews to capture real user feedback and emotional tone. Customer reviews are preprocessed by removing noise, tokenizing text, and applying lemmatization. Sentiment polarity is calculated using Vader from NLTK or TextBlob, and product sentiment scores are combined with similarity-based recommendation techniques. The system ranks products not only based on user preferences but also based on positive sentiment trends. Using content-based filtering, the system analyzes product descriptions and review sentiment to recommend similar yet highly appreciated products. The entire system is deployed using Streamlit, offering a user-friendly interface with recommendation visualization and sentiment insights. This approach improves recommendation quality, increases customer satisfaction, and helps users make better purchasing decisions.

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