Moaaz Ahmad

Academic

Career

Verbatim text

New Product Idea Generation Through Consumer Sentiment 
Analysis Using Machine Learning Techniques 
The project aims to leverage machine learning and sentiment analysis to generate new 
product ideas, which are critical for remaining competitive in today's market. Businesses can 
make informed, customer-centric decisions during the product development process by 
employing sentiment analysis, which extracts insights from customer data sources like 
preferences and views. The research questions focus on identifying optimal sentiment 
analysis frameworks, understanding challenges in customer review analysis, assessing 
techniques relevant to the portable tech industry, and analyzing the impact of aligning 
product development with consumer sentiments on environmental responsibility. 
The research being conducted is significant for several reasons. For starters, it fills gaps in 
sentiment analysis research, notably in the portable tech industry, by providing a powerful 
tool for designing new goods that are in line with user preferences. Second, by linking 
product creation with consumer opinion, businesses may reduce waste and increase 
environmental responsibility. The research's systematic strategy reduces the risks of 
innovation while ensuring product perfection. Furthermore, knowing customer feelings and 
preferences is critical for successful product creation, therefore sentiment analysis is a vital 
tool in this process. 
 
 
 
Technology Used: 
KNIME 
Selenium 
Python 
Supervisor Name: 
Mr. Hammad Majeed 
Ms. Farah Naz 
Group Members:  
Moaaz Ahmad (20I-0005)

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