International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
www.irjet.net
Sentiment Analysis in POS system using machine learning algorithms Yuvraj Sharma1, Bhavik Chawan2, Naved Akhtar3, Prof.Sonali Karthik4 1 2 3 Student, Information Technology Theem college of engineering Mumbai, India. 4 Professor, Information Technology Theem college of engineering Mumbai, India.
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Abstract –The focus of the research is to propose the integration of machine learning techniques into point-of-sale (POS) systems in order to boost client experiences and optimize performance. These systems can forecast demand, optimize inventory levels, and customize consumer experiences by evaluating past sales data, predictive models, and natural language processing. The interfaces for recommendation systems offer sales personnel and consumers clear recommendations that work with a variety of devices. With data privacy and smooth integration upheld, this integration is a strategic move toward modernizing retail operations, encouraging loyalty, and boosting revenue development. This information can be used to continuously refine product offerings and identify areas for improvement, enhancing the overall shopping experience. Balancing technical finesse with user-centric design, the recommendation system interfaces provide intuitive and actionable suggestions to both customers and sales staff. The system's adaptive capabilities cater to diverse devices, fostering engagement through user- friendly interfaces. Ultimately, the integration of a recommendation system into a POS project represents a strategic step forward in modernizing retail operations. It redefines the shopping experience by marrying technology with customer-centricity, fostering loyalty, and fueling revenue growth while keeping data protection and seamless integration at its core.
and customer-centric retail operations. The integration of advanced data analysis, machine learning algorithms, and user experience enhancement to create a dynamic shopping environment. The recommendation system's primary objective is to provide personalized product suggestions to customers based on their preferences, purchase history, and browsing behavior. This involves the collection, analysis, and utilization of customer data to offer targeted recommendations, ensuring a more engagement.
1.1 Paper Reviewed Proposed a deep learning-based medical material inventory management model is constructed through the reasonable classification of material management methods. This model effectively utilizes the data by analyzing disaster data in different regions and establishes a corresponding inventory management model according to the classification standards.[1] They have implemented low efficiency of knowledge acquisition; this paper proposes a knowledge service framework based on case set. Three knowledge retrieval methods are designed based on parts keywords, customer orders and manufacturing processes. Additionally, a VSM based (vector space model) knowledge recommender method.[2]
Key Words: Sentiment Analysis,POS system, Inventory management, Sales Forecasting, Recommendation.
A step-enhancement of memory retention (SEMR) model which integrates the cross-enhancement- effects of multiple historical behaviors under different time windows to characterize user interest. In addition, we use some extended correction methods to eliminate the effect of discontinuous records. Numerical experiments using real TV viewing data validate the efficiency of our proposed model and methods, which reduce the average prediction error to 0.3, outperforming the traditional models by around 50%.[3]
1.INTRODUCTION This Point of Sale (POS) system proposed in this project marks a significant advancement in retail technology, aimed at revolutionizing the traditional checkout process. Our POS system integrates cutting-edge features, including sales forecasting through algorithm and dynamic product recommendations powered by Convolutional Neural Network (CNN) algorithms. By leveraging these advanced algorithms, the system not only enhances transaction accuracy and inventory management but also creates a personalized shopping experience for customers. The integration of ARIMA allows for precise sales predictions, optimizing inventory levels and operational efficiency. Simultaneously, the CNN algorithm processes product images, providing customers with real-time, visuallydriven recommendations. As businesses embrace digital transformation, our POS system emerges as a crucial tool, fostering a seamless blend of technology, data analytics,
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Impact Factor value: 8.226
Sentiment analysis of a large number of user reviews on e-commerce platforms can effectively improve user satisfaction. This paper proposes a new sentiment analysis model-SLCABG, which is based on the sentiment lexicon and combines Convolutional Neural Network (CNN) and attention- based Bidirectional Gated Recurrent Unit (BiGRU). In terms of methods, the SLCABG model combines the advantages of sentiment lexicon and deep learning.[4]
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