Sentiment Cube Data Structure for Multi-Dimensional Analysis
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Solution Overview
Problem
Current sentiment analysis systems lack an effective data structure and operations to analyze sentiments at various levels of granularity and hierarchy, making it difficult to derive insights from large volumes of sentiment data across different dimensions and perspectives.
Innovation Solution
A sentiment cube data structure is introduced, enabling Business Intelligence and OLAP queries to be formulated and executed, allowing for the analysis of sentiments across different categories, topics, and levels of granularity, with operations such as roll-ups, drill-downs, and correlations between streaming and stored data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sentiment analysis methods are used, then sentiment data can be collected, but it becomes difficult to analyze sentiments at various levels of granularity and hierarchy
Solution Approach 1:
The patent introduces a multi-dimensional sentiment cube data structure that adds hierarchical dimensions (product, category, industry, geography, time) to traditional sentiment analysis. This allows sentiment data to be analyzed from multiple perspectives simultaneously, transforming the analysis from flat 2D views to multi-dimensional cubic views, thereby resolving the contradiction between analysis granularity and data structure complexity.
Solution Approach 2:
The sentiment cube is segmented into multiple hierarchical levels including product-level, category-level, and industry-level sentiments. Each level can be independently analyzed while maintaining relationships with other levels. This segmentation allows precise sentiment analysis at different granularities without requiring a single complex monolithic structure.
2Loss of information
If comprehensive sentiment data from multiple sources is aggregated, then more insights can be derived, but the complexity of managing and analyzing the data increases
Solution Approach 1:
The patent merges sentiment data from multiple sources (social media, reviews, surveys) and multiple dimensions (product, category, geography, time) into a unified sentiment cube structure. This consolidation allows comprehensive information to be stored in an organized manner, reducing the complexity of managing disparate data sources while maintaining information completeness.
Solution Approach 2:
The sentiment cube serves multiple functions simultaneously: it stores raw sentiment data, provides hierarchical aggregation, enables comparative analysis across dimensions, and supports various query types. This multi-functionality reduces the need for separate systems for different analysis tasks, thereby reducing overall data management complexity.
3Measurement precision
If sentiment analysis is performed at multiple levels of hierarchy, then more detailed insights are obtained, but the computational operations become more complex
Solution Approach 1:
The patent pre-computes and stores aggregated sentiment values at multiple hierarchical levels within the sentiment cube structure. When analysis is needed, these pre-computed values are readily available, eliminating the need for time-consuming recomputation. This preliminary action significantly reduces computation time while maintaining detailed insight capabilities.
Solution Approach 2:
The sentiment cube implements a nested hierarchical structure where product-level sentiments are nested within category-level sentiments, which are nested within industry-level sentiments. This nesting allows efficient computation by leveraging the hierarchical relationships, where computations at higher levels can utilize results from lower levels, reducing overall computational complexity and time.
Data Source
AI summary
A sentiment cube system is disclosed. In one example, the system discloses a sentiment storage, including a sentiment cube data structure having a set of cells arranged by a set of dimensions. The system includes a computer programmed with executable instructions which operate a set of modules, wherein the modules comprise: a sentiment storage module which receives sentiment values associated with a set of entity features, and then populates a hierarchy of the cells in the sentiment cube with the sentiment values. A sentiment analysis module effecting a set of operations on the sentiment cube.