Cloud Sentiment Analysis System for Rapid Phrase Extraction
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Solution Overview
Problem
Conventional sentiment analysis techniques are time-consuming and require human intervention to analyze vast collections of online data from product reviews, blog posts, and social media, as they need to be performed on local computers with installed software, limiting scalability and efficiency.
Innovation Solution
A cloud-based system and method for sentiment analysis that aggregates and classifies text-based comments, generates common phrases, and produces graphical outputs, allowing for quick feedback on sentiment and topic analysis without the need for local software installation, using techniques like lemmatization, tri-gram generation, and machine learning for sentiment classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional sentiment analysis techniques are used on local computers with installed software, then sentiment analysis can be performed, but the process is time-consuming and requires human intervention
Solution Approach 1:
The patent replaces manual human review with automated machine learning models and natural language processing algorithms. The system automatically classifies sentiment polarity, extracts phrases, and generates visualizations without requiring human intervention in the analysis process, thereby dramatically increasing productivity and eliminating time loss associated with manual review.
Solution Approach 2:
The system performs sentiment analysis autonomously by aggregating text data, processing it through ML models, generating phrases, creating visualizations, and exporting results all in one automated workflow. The cloud-based architecture enables the system to serve itself by managing the entire analysis pipeline without external human operation, resolving the contradiction between automation and manual intervention.
2Adaptability or versatility
If conventional sentiment analysis software is installed on each local computer, then sentiment analysis can be performed, but scalability is limited
Solution Approach 1:
The patent introduces a cloud-based server as an intermediary between users and the sentiment analysis processing. Instead of installing software on each local computer, users access the system through a web interface or API, and all heavy processing occurs on the remote server. This eliminates device complexity requirements while enabling unlimited scalability across different devices and locations.
Solution Approach 2:
The cloud-based platform provides universal access to sentiment analysis capabilities across multiple devices, operating systems, and locations through a single centralized system. The system handles data aggregation, processing, visualization, and export functions universally, removing the need for separate software installations and enabling scalable deployment without increasing device complexity.
3Measurement precision
If vast collections of online data are analyzed manually, then detailed sentiment understanding can be achieved, but the process takes from minutes to days to complete
Solution Approach 1:
The patent segments the vast collection of text data into individual units (comments, reviews, posts) that can be processed independently by the machine learning model. The system divides the analysis task into discrete processing steps: data aggregation, sentiment classification, phrase extraction, and visualization generation. This segmentation enables parallel processing of multiple data points simultaneously, achieving both high precision through individual analysis and high productivity through batch processing.
Solution Approach 2:
The system changes the processing parameters by using automated machine learning algorithms with configurable precision thresholds. The ML models can adjust classification sensitivity and phrase extraction parameters to maintain measurement precision while processing data at machine speed. This allows the system to analyze vast collections of data rapidly without sacrificing sentiment classification accuracy, resolving the contradiction between precision and productivity.
4Productivity
If sentiment analysis is performed using local software, then data can be processed, but devices with limited processing power cannot effectively perform the analysis
Solution Approach 1:
The cloud-based server acts as an intermediary that handles all computationally intensive processing tasks. Users with devices of any processing capability can access the full sentiment analysis functionality through the web interface or API. The server performs data aggregation, ML-based sentiment classification, phrase extraction, and visualization generation, enabling high productivity on devices with limited processing power without increasing their device complexity.
Data Source
AI summary
A method of providing sentiment analysis includes aggregating, by a processor, a plurality of text-based comments, classifying, by the processor, the plurality of text-based comments as being associated with a polarity of sentiment, and generating, by the processor, a plurality of phrases from the plurality of text-based comments. The method also includes identifying, by the processor, a predetermined number of most common phrases for a particular polarity of sentiment from the plurality of phrases and outputting, by the processor, a graphic that includes the predetermined number of most common phrases for the particular polarity of sentiment.