Customer Sentiment Analysis for Demand Forecasting of Electronic Devices Using Machine Learning Techniques

Customer Sentiment Analysis for Demand Forecasting of Electronic Devices Using Machine Learning Techniques

Ram Chaudhari
Dept. of Computer Engineering Pune Institute of Computer Technology

Pune, India ram.kb19@gmail.com

B. A. Sonkamble
Dept. of Computer Engineering Pune Institute of Computer Technology

Pune, India basonkamble@pict.edu

Shaunak Torgatti
Dept. of Computer Engineering Pune Institute of Computer Technology

Pune, India shaunakht@gmail.com

Shreyas Umbardand
Dept. of Computer Engineering Pune Institute of Computer Technology

Pune, India shreyass.umbardand@gmail.com

Abstract—The growing influence of e-commerce and social me- dia has made traditional demand forecasting methods insufficient due to their limited responsiveness to real-time consumer senti- ment. This paper presents a sentiment-driven demand forecasting framework that combines web scraping, natural language pro- cessing, and machine learning. Product reviews from Amazon and tweets are extracted using BeautifulSoup, analyzed using BERT, NLTK, SpaCy, and a custom LSTM model. Sentiment scores are integrated into a pooled machine learning model alongside historical sales and seasonal data. Bayesian inference is then applied to perform sentiment-weighted product allocation across regions. Experimental results demonstrate improved forecasting accuracy, supporting more adaptive inventory and marketing strategies.

Keywords - Demand forecasting, sentiment analysis, BERT, Bayesian inference, machine learning, NLP.