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## User: kumar-ravi

### Title: CUDA-Self-Organizing feature map based visual sentiment analysis of bank customer complaints for Analytical CRM

wealth of customer feedback data which has valuable applications to Customer
Relationship Management (CRM). Analyzing customer grievances data, is paramount
as their speedy non-redressal would lead to customer churn resulting in lower
profitability. In this paper, we propose a descriptive analytics framework
using Self-organizing feature map (SOM), for Visual Sentiment Analysis of
customer complaints. The network learns the inherent grouping of the complaints
automatically which can then be visualized too using various techniques.
Analytical Customer Relationship Management (ACRM) executives can draw useful
business insights from the maps and take timely remedial action. We also
propose a high-performance version of the algorithm CUDASOM (CUDA based Self
Organizing feature Map) implemented using NVIDIA parallel computing platform,
CUDA, which speeds up the processing of high-dimensional text data and
generates fast results. The efficacy of the proposed model has been
demonstrated on the customer complaints data regarding the products and
services of four leading Indian banks. CUDASOM achieved an average speed up of
44 times. Our approach can expand research into intelligent grievance redressal
system to provide rapid solutions to the complaining customers.

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