• S mani shankar
• R. Shreya
• S.Harshitha
• V. Swathi

In today's digital economy, customers express their product experiences and concerns through online reviews. Extracting improvement ideas from these reviews offers valuable insights into market expectations. While previous studies have focused on identifying customer needs from reviews, they often overlook the specific usage context. This study introduces a novel method that considers both context and product functions derived from reviews. By mining linguistic patterns, customer needs are determined by combining context information and product functions using semantic embedding and clustering. A case study on the Amazon Echo series validates this approach, identifying 1430 distinct customer needs, offering valuable input for product enhancement. This study pioneers integrating context information into identifying customer needs, providing a promising approach for future product planning in the e-commerce industry

"Acquiring interests based on context of customer needs based on online product reviews!", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.m139-m141, April-2024, Available :http://www.jetir.org/papers/JETIR2404C19.pdf

"Acquiring interests based on context of customer needs based on online product reviews!", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppm139-m141, April-2024, Available at : http://www.jetir.org/papers/JETIR2404C19.pdf