Cloud Sentiment Analysis System with Dynamic Clustering
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Solution Overview
Problem
Current business intelligence systems require extensive expertise and customization, making them costly and difficult to implement and maintain, especially for small to medium-sized businesses, due to the need for custom coding and high-end analytics skills, and they struggle with accurately analyzing vast amounts of consumer sentiment data from diverse sources.
Innovation Solution
The development of cloud-based consumer sentiment analysis systems that integrate data from multiple sources using cloud computing techniques, employing clustering algorithms and dynamic sentiment calculation to generate consumer segments and visualize correlations, allowing for plug-and-play implementation of subcomponents via APIs, abstracting away complex technology details and enabling user-friendly data analysis without requiring advanced technical knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional business intelligence systems are used, then data analysis capability is provided, but system complexity and implementation cost increase significantly
Solution Approach 1:
The patent introduces an intermediary layer (cloud-based processing platform, automated ETL tools, pre-configured analytics engines) that mediates between raw data sources and business users. This intermediary handles the complexity of data integration, cleaning, and analysis automatically, allowing businesses to access advanced analytics without directly managing system complexity.
Solution Approach 2:
The system enables self-service capabilities through automated data pipeline configuration, self-provisioning of analytics resources, and automated reporting. Businesses can independently set up and manage their own analytics systems without requiring expert system integrators, reducing both complexity and implementation costs.
2Ease of manufacture
If expert system integrators or highly skilled personnel are used, then system implementation is achieved, but implementation cost increases
Solution Approach 1:
The patent implements self-service mechanisms including automated system provisioning, self-configurable data pipelines, and automated deployment tools that allow businesses to implement analytics systems independently. This eliminates the need for expensive expert system integrators while maintaining implementation quality through automated best practices and validation.
Solution Approach 2:
The system allows businesses to implement analytics by changing parameters (data sources, analysis types, output formats) rather than requiring changes to the underlying system architecture. This parameter-based configuration approach enables implementation by less skilled personnel while maintaining system robustness.
3Adaptability or versatility
If custom coding and heavy customization are required, then system functionality is enhanced, but ease of operation decreases
Solution Approach 1:
The patent implements a universal platform that handles multiple analytics functions (data integration, cleaning, analysis, visualization, reporting) through standardized interfaces and pre-configured components. This universal approach provides enhanced functionality without requiring custom coding, as the platform can be configured for different needs through parameter settings rather than code changes.
Solution Approach 2:
The system provides dynamic configurability where businesses can adjust analysis parameters, data sources, and output formats without recoding. The platform dynamically adapts to different business needs through configuration interfaces, maintaining versatility while preserving ease of operation through automated processing of configuration changes.
4Reliability
If new technologies like big data or cloud computing are adopted, then data analysis capability is improved, but expertise requirement and cost increase
Solution Approach 1:
The patent introduces an intermediary cloud-based platform that mediates between businesses and complex big data technologies. This platform abstracts the complexity of distributed computing, data storage, and advanced analytics algorithms, allowing businesses to leverage powerful technologies without requiring deep expertise in their operation and maintenance.
Solution Approach 2:
The system implements self-service capabilities that automatically handle technology management tasks including resource provisioning, scaling, security configuration, and updates. This allows businesses to adopt advanced technologies while the system automatically manages the expertise-intensive aspects, reducing both expertise requirements and costs.
Data Source
AI summary
The present invention relates to systems and methods for cloud based consumer sentiment analysis with social insights. Data is integrated from a plurality of data sources, including a structured data source, an unstructured data source, a social data source, and a syndicated data source. Key attributes are selected from the integrated data, and may be name value pair requests. From these key attributes, consumer segments, sentiments and attribute correlations may be generated. The segments are generated from the social data. The correlation is generated using clustering algorithms. In some embodiments, generating sentiment and generating correlations dynamically utilizes models according to attributes of the integrated data. Polarity, emotion and topicality may be calculated for the generation of visualizations.