Cloud Sentiment Analysis System for Real-Time Text Polarity Detection
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Solution Overview
Problem
Conventional sentiment analysis techniques require local software installations on computing devices and are time-consuming, as they rely on human interpretation of online text for sentiment recognition, which is inefficient and not scalable for real-time analysis.
Innovation Solution
A cloud-based sentiment analysis system using machine learning that trains a backend service model to analyze sentiment polarity in real-time, allowing instantaneous analysis without the need for local software installations, and provides tailored feedback through a frontend website.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional sentiment analysis software is installed on local computers, then sentiment analysis can be performed, but the system becomes complex and requires software installation on each device
Solution Approach 1:
The patent introduces a cloud-based intermediary service that performs sentiment analysis remotely. Instead of installing software on each local computer, users access the analysis service through a web interface. The cloud server receives text input, processes it through the trained sentiment analysis model, and returns results, eliminating the need for local software installation while maintaining analysis functionality.
2Productivity
If human reading is used for sentiment analysis, then analysis accuracy can be maintained, but the process is time-consuming and not scalable
Solution Approach 1:
The patent replaces the mechanical process of human reading and interpretation with an automated computer-based system. A machine learning model trained on sentiment analysis algorithms processes text automatically, eliminating the need for manual human review. This substitution enables instantaneous analysis of large volumes of text that would be impossible to process manually in real-time.
Solution Approach 2:
The sentiment analysis model is pre-trained using a comprehensive dataset of text samples with known sentiment labels. During the training phase, the system learns patterns and characteristics of positive, negative, and neutral sentiments from extensive text data. This preliminary training enables the model to perform accurate real-time analysis without requiring human experts to review each text item individually during operation.
3Speed
If real-time sentiment analysis is implemented, then responsiveness improves, but computational resources and system complexity increase
Solution Approach 1:
The patent transitions the sentiment analysis system from a local dimension to a cloud-based dimension. By moving computational resources to a remote server, the system achieves real-time processing capability without increasing local device complexity. The cloud infrastructure provides the necessary computational power and scalability, allowing the frontend application to remain simple while maintaining high-speed analysis performance.
Data Source
AI summary
A method of providing sentiment analysis includes training a computer system to identify a polarity of a sentiment for a plurality of phrases and receiving, at the computer system, an input from a website. The input includes a text string including a phrase. The method includes determining, by the computer system, the polarity of the sentiment of the phrase by comparing the phrase to the plurality of phrases and sending, by the computer system, a command to the website that causes the website to display predetermined content based on the determined polarity of the sentiment of the phrase.