The increasingly widespread use of the internet allows companies to gain insights regarding product development. Online reviews can provide an overview of how customers need a product, and this can help companies to understand what customers like and dislike about their products. This paper proposes a methodology for identifying consumer needs by analysing online reviews using the lexicon-based method and topic modeling with Non-negative Matrix Factorization (NMF). The main idea of this paper is to translate the results of aspect-based sentiment analysis into consumer needs with its priority ranking. The methodology is applied in an online review of a laptop product. Based on the study results, it is shown that the observed features based on customer reviews are battery, storage, screen, price, performance, keyboard, and design, with each topic having overall positive sentiment. Based on the topics and their sentiment, the prioritized needs are good screen strength, a fast and responsive processor, and responsive keyboard keys.