TR-4910: Sentiment Analysis from Customer Communications with NetApp AI
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- AI Converged Infrastructures
Data Pipelines, Data Lakes and Management
- NetApp AI Control Plane
- Distributed training in Azure - Click-Through Rate Prediction
- Conversational AI using NVIDIA
- Anthos with NetApp
- DevOps with NetApp Astra
Red Hat OpenShift with NetApp
- NetApp Storage Integrations Overview
Solution Validation and Use Cases
- Red Hat OpenShift Virtualization with NetApp ONTAP
- VMware Tanzu with NetApp
- Data Migration and Data Protection
- Oracle Database
- SnapCenter for Databases
- Modern Data Analytics
NetApp Hybrid Multicloud with VMware
- VMware for Public Cloud
- NetApp for GCP / GCVE
- NetApp Hybrid Multicloud with Red Hat OpenShift
- VMware Virtualization for ONTAP
- Virtual Desktops
- Artificial Intelligence
Rick Huang, Sathish Thyagarajan, and David Arnette, NetApp
Diego Sosa-Coba, SFL Scientific
This technical report provides design guidance for customers to perform sentiment analysis in an enterprise-level global support center by using NetApp data management technologies with an NVIDIA software framework using transfer learning and conversational AI. This solution is applicable to any industry wanting to gain customer insights from recorded speech or text files representing chat logs, emails, and other text or audio communications. We implemented an end-to-end pipeline to demonstrate automatic speech recognition, real-time sentiment analysis, and deep-learning natural-language- processing model- retraining capabilities on a GPU-accelerated compute cluster with NetApp cloud-connected all flash storage. Massive, state-of-the-art language models can be trained and optimized to perform inference rapidly with the global support center to create an exceptional customer experience and objective, long-term employee performance evaluations.
Sentiment analysis is a field of study within Natural Language Processing (NLP) by which positive, negative, or neutral sentiments are extracted from text. Conversational AI systems have risen to a near global level of integration as more and more people come to interact with them. Sentiment analysis has a variety of use cases, from determining support center employee performance in conversations with callers and providing appropriate automated chatbot responses to predicting a firm’s stock price based on the interactions between firm representatives and the audience at quarterly earnings calls. Furthermore, sentiment analysis can be used to determine the customer’s view on the products, services, or support provided by the brand.
This end-to-end solution uses NLP models to perform high level sentiment analysis that enables support-center analytical frameworks. Audio recordings are processed into written text, and sentiment is extracted from each sentence in the conversation. Results, aggregated into a dashboard, can be crafted to analyze conversation sentiments, both historically and in real-time. This solution can be generalized to other solutions with similar data modalities and output needs. With the appropriate data, other use cases can be accomplished. For example, company earnings calls can be analyzed for sentiment using the same end-to-end pipeline. Other forms of NLP analyses, such as topic modeling and named entity recognition (NER), are also possible due to the flexible nature of the pipeline.
These AI implementations were made possible by NVIDIA RIVA, the NVIDIA TAO Toolkit, and the NetApp DataOps Toolkit working together. NVIDIA’s tools are used to rapidly deploy highly performant AI solutions using prebuilt models and pipelines. The NetApp DataOps Toolkit simplifies various data management tasks to speed up development.
Businesses see value from an employee-assessment and customer-reaction tool for text, audio, and video conversation for sentiment analysis. Managers benefit from the information presented in the dashboard, allowing for an assessment of the employees and customer satisfaction based on both sides of the conversation.
Additionally, the NetApp DataOps Toolkit manages the versioning and allocation of data within the customer’s infrastructure. This leads to frequent updates of the analytics presented within the dashboard without creating unwieldy data storage costs.