Sentiment Analysis Pipeline for Automated Business Rule Triggering
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Solution Overview
Problem
The aggregation of user sentiments from various media outlets results in a large, unorganized body of data, necessitating effective filtering and analysis techniques to extract actionable insights for businesses.
Innovation Solution
A sentiment analysis process involving a corpus builder to sort data, an opinion mining platform to identify keywords and generate sentiment metrics, and a rules extractor program to determine actions through a business rules engine, leveraging natural-language processing for automated decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sentiment data is aggregated from various media outlets, then the quantity of sentiment data increases, but the organization and manageability of the data deteriorates
Solution Approach 1:
The patent segments the unorganized sentiment data into structured components by extracting specific fields (sentiment, topic, source, timestamp) from unstructured text. The corpus builder divides incoming data streams into manageable sorted data units that can be processed independently, transforming the undifferentiated mass of sentiment data into organized, queryable records.
Solution Approach 2:
The patent introduces an intermediary processing layer (corpus builder and opinion mining platform) between raw sentiment data and business applications. This intermediary layer sorts, cleans, and structures the data, acting as a mediator that transforms unorganized sentiment inputs into organized, actionable insights without requiring businesses to directly manage the raw data complexity.
2Measurement precision
If manual analysis of sentiment data is performed, then the precision of sentiment extraction improves, but the time required for processing increases
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computational systems. The opinion mining platform uses natural language processing algorithms and sentiment analysis engines to automatically extract and score sentiments, substituting human analysts with machine-based processing that maintains precision while dramatically reducing processing time.
Solution Approach 2:
The system enables self-service sentiment analysis where the data automatically sorts itself through the corpus builder, and the opinion mining platform automatically extracts and scores sentiments without human intervention. The business rules engine then automatically acts on the extracted insights, creating a self-service pipeline that eliminates manual processing while maintaining analytical precision.
3Productivity
If automated business rules engine is implemented, then the productivity of decision-making improves, but the complexity of the system increases
Solution Approach 1:
The patent implements preliminary action by pre-defining business rules and thresholds before sentiment analysis begins. The system is configured with predetermined conditions (e.g., sentiment thresholds, action triggers) that are established in advance, allowing the business rules engine to automatically execute decisions without requiring complex real-time analysis or ad-hoc rule creation, thereby simplifying the operational complexity.
4Measurement precision
If comprehensive sentiment analysis is performed on all data, then the accuracy of insights improves, but the computational resources required increases
Solution Approach 1:
The patent extracts only the essential sentiment information needed for business decisions rather than analyzing all aspects of the data. The opinion mining platform selectively extracts sentiment scores, topics, and key metrics from the data stream, taking out only the relevant insights required for triggering business rules, thereby reducing computational overhead while maintaining insight accuracy.
Data Source
AI summary
A method for analyzing and implementing sentiments includes sorting data from the data stream into sorted data by using a corpus builder. The sorted data is then input into an opinion mining platform where selected content is obtained based on the identification of keywords present in the sorted data. A sentiment extraction program generates sentiment metrics based on analysis of the selected content. A rules extractor program determines, based on the sentiment metrics satisfying rules, if actions are to be performed by a business rules engine.