| dc.contributor.author | Rajan, Prateek | |
| dc.description.abstract | In the present world of ecommerce more and more products are purchased and sold online then via any other medium. With such massive drive in online shopping more and more information is being added every day on web regarding the products and how good or bad are they. From the perspective of seller (such as Amazon) this information is very vital as this insight could be very helpful in making various decisions regarding inventory management, product pricing and so on. But the problem that arises in this context is the sheer volume of the reviews being added. In this paper we have proposed a way of extracting the semantics out of the reviews via use of various linguistic and statistical techniques. The idea is to extract the relevant information from the review and represent it in most concise format to make it more suitable for later processing. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU Policy 190.6.2 | |
| dc.title | Review Mining: Hierarchy Generation for Online Reviews | en_US |
| dc.type | Master's paper | en_US |
| dc.date.accessioned | 2015-12-23T18:11:26Z | |
| dc.date.available | 2015-12-23T18:11:26Z | |
| dc.date.issued | 2015 | |
| dc.identifier.uri | http://hdl.handle.net/10365/25511 | |
| dc.subject.lcsh | Data mining. | en_US |
| dc.subject.lcsh | User-generated content -- Research. | en_US |
| dc.subject.lcsh | Recommender systems (Information filtering) | en_US |
| dc.subject.lcsh | Consumer satisfaction -- Data processing. | en_US |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Computer Science | en_US |
| ndsu.program | Computer Science | en_US |
| ndsu.advisor | Jin, Wei | |
