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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Saitoh, F. |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Ind. & Syst. Eng., Aoyama Gakuin Univ., Sagamihara, Japan (Saitoh, F.) |
| Abstract | In recent times, it has become easier to collect large quantities of customer reviews of products and services through the Internet. Thus, text-mining has become evermore important for various businesses. However, current techniques to visualize the correspondence relation between customer reviews and evaluation information are insufficient. The purpose of this paper is to propose a new method of visualizing information using a Self-organizing Map(SOM) that is robust for text data that is non-linear and multi-collinear. Our method involves, probabilistic Latent Semantic Indexing (pLSI) which does not require weighting for the dimension contraction of a word vector. Furthermore, we also propose a method to visualize the distribution of evaluation information on SOM. In order to assign a suitable value to dead nodes and nodes without evaluation values, we redefine the interpolation formula for the evaluation value. To confirm the effectiveness and accuracy of our proposal, we use our method to visualize customer review data on a major E-Commerce website. |
| Starting Page | 176 |
| Ending Page | 181 |
| File Size | 1188948 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479938407 |
| DOI | 10.1109/SMC.2014.6973903 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-10-05 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Data visualization Vectors Resonant frequency Business Semantics Data mining Robustness |
| Content Type | Text |
| Resource Type | Article |
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