The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Automatic Categorization of Reviews and Opinions of Internet E-Shopping Customers
Abstract
E-shopping customers, blog authors, reviewers, and other web contributors can express their opinions of a purchased item, film, book, and so forth. Typically, various opinions are centered around one topic (e.g., a commodity, film, etc.). From the Business Intelligence viewpoint, such entries are very valuable; however, they are difficult to automatically process because they are in a natural language. Human beings can distinguish the various opinions. Because of the very large data volumes, could a machine do the same? The suggested method uses the machine-learning (ML) based approach to this classification problem, demonstrating via real-world data that a machine can learn from examples relatively well. The classification accuracy is better than 70%; it is not perfect because of typical problems associated with processing unstructured textual items in natural languages. The data characteristics and experimental results are shown.
Related Content
|
Aynetu Terefe, Shashi Kant, Metasebia Adula, Tafese Niguse.
© 2026.
26 pages.
|
|
Tanya, Nitin Pathak, Priyanka Chugh.
© 2026.
32 pages.
|
|
Nitika Sharma, Paras Sarjolta.
© 2026.
18 pages.
|
|
Manoj Govindaraj, Ravishankar Krishnan, L. Anitha, G. M. Shaju, Chandramowleeswaran Gnanasekaran, Jenifer Lawrence.
© 2026.
30 pages.
|
|
Ravishankar Krishnan, Navaneetha Krishnan Rajagopal.
© 2026.
28 pages.
|
|
Kriti Kishor, Sanjeev Kumar Bansal, Stefano Bresciani.
© 2026.
14 pages.
|
|
Shashi Kant, Tamire Ashuro, Metasebia Adula.
© 2026.
30 pages.
|
|
|