Social media has reinforced consumer power, allowing customers to obtain more and more information about businesses and products, voice their opinions as well as convey their grievances. In this article, we introduce a descriptive analytics system for visual sentiment analysis of customer complaints using the self-organizing feature map (SOM). The network eventually learns the underlying classification of grievances that can then be visualized using different methods too. Executives of analytical customer relationship management (ACRM) will derive valuable business insights from the maps and enforce prompt remedial measures. We also propose a high-performance version of the CUDASOM (Compute Unified Device Architecture (CUDA)-based self-organizing function Map) algorithm implemented using NVIDIA®'s parallel computing platform, CUDA, which accelerates the processing of high-dimensional text data and produces fast results. The effectiveness of the proposed model has been demonstrated on a dataset of customer complaints about the products and services of four leading Indian banks. CUDASOM recorded an average speedup of 44 times. Our technique can expand studies into smart grievance redressal systems to provide the complaining consumers with quick solutions.

Chapter Contents:

• 12.1 Introduction
• 12.2 Motivation
• 12.3 Contribution
• 12.4 Literature survey
• 12.5 Description of the techniques used
• 12.5.1 Self-organizing feature maps
• 12.5.2 Compute Unified Device Architecture
• 12.6 Proposed approach
• 12.6.1 Text preprocessing
• 12.6.2 Implementation of CUDASOM
• 12.6.3 Segmentation of customer complaints using SOM
• 12.7 Experimental setup
• 12.7.1 Dataset details
• 12.7.2 Preprocessing steps
• 12.7.3 CUDA setup
• 12.8 Results and discussion
• 12.8.1 Segmentation of customer complaints using CUDASOM
• 12.8.2 Performance of CUDASOM
• 12.9 Conclusions and future directions
• Acknowledgments
• References

Inspec keywords: pattern classification; consumer behaviour; self-organising feature maps; customer relationship management; parallel algorithms; bank data processing; social networking (online); parallel architectures; sentiment analysis

Other keywords: analytical customer relationship management; descriptive analytics system; parallel self-organizing maps; high-dimensional text data processing; CUDA-based self-organizing function map algorithm; social media; Indian bank customer complaints; compute unified device architecture; smart grievance redressal systems; visual sentiment analysis; SOM; NVIDIA parallel computing platform; CUDASOM; grievance classification; ACRM

Subjects: Parallel software; Marketing computing; Natural language interfaces; Document processing and analysis techniques; Financial computing; Information networks; Neural nets; Parallel architecture