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@article{172828,
        author = {Ram Chaudhari and Shreyas Umbardand and Shaunak Torgatti and Balwant Sonkamble},
        title = {Customer Sentiment Analysis for Demand Forecasting of Electronic Devices Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {2976-2981},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172828},
        abstract = {This paper presents a theoretical framework for predicting the demand for electronic devices by leveraging customer sentiment analysis and machine learning techniques. With the rapid evolution of electronic gadgets, forecasting market trends is crucial for staying competitive. Customer sentiment, derived from reviews and social media feedback, plays a pivotal role in predicting consumer behavior. In this work, we propose a comprehensive solution combining natural language processing (NLP) and machine learning models to predict demand trends. The proposed method offers actionable insights to enhance decisionmaking and optimize distribution strategies in the electronics industry.},
        keywords = {Sentiment Analysis, Demand Forecasting, Machine Learning, Natural Language Processing, Electronic Devices, Data-driven Decision Making},
        month = {March},
        }