Abstract— Now days, purchasing branded Desktop PC from a single Vendor is getting expensive day by day. Also consumers are required to rely on single Vendor for their whole Desktop PC and thereby compromising on the hardware so as to reduce cost. To overcome this problem a new approach was developed i.e. to assemble the PC by purchasing its essential component from different vendors. A large number of websites allow users to post reviews about product they bought. People who find it difficult to understand the technical specification of a product often tends to read reviews. There are thousands of reviews of customers available on different e-commerce portal related to a product. So it is difficult for the customers to get an idea about the product from wide range of reviews. In this project we aim to summarize the customer‟s reviews in factual form using part-ofspeech tagger (POS), Lexicon algorithm, and Naive Bayesian classifier which are all based on Natural language processing (NLP).We have developed a rating system which rates each computer peripherals based on its performance & cost and thereby calculating the final rating of assembled system virtually so that consumers can judge whether the assembled product meets all the requirement as expected. Keywords- PC Assembly, Reviews, POS, Opinion Mining, Extraction, Java, Perl, XML, Products
James, H. (2018). PC Assembly. Afribary. Retrieved from https://afribary.com/works/pc-assembly-7352
James, Henry "PC Assembly" Afribary. Afribary, 29 Jan. 2018, https://afribary.com/works/pc-assembly-7352. Accessed 24 Nov. 2024.
James, Henry . "PC Assembly". Afribary, Afribary, 29 Jan. 2018. Web. 24 Nov. 2024. < https://afribary.com/works/pc-assembly-7352 >.
James, Henry . "PC Assembly" Afribary (2018). Accessed November 24, 2024. https://afribary.com/works/pc-assembly-7352