Sentiment Analysis System Using Topic Vectors and Control Signals
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Solution Overview
Problem
The large volume of social media data makes human review impractical and unreliable for analyzing public sentiment, as it is prone to bias and subjective interpretation, necessitating a machine-based system for sentiment analysis.
Innovation Solution
A method and system that assign topic vectors to social media comments, classify them using Latent Dirichlet Allocation, calculate sentiment scores, and generate control signals based on average sentiment scores, allowing for the visualization of sentiment over time and triggering actions when criteria are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human review of social media postings is used, then subjective interpretation and bias can be eliminated through human judgment, but the process becomes impractical due to large data volume and unreliable due to human bias
Solution Approach 1:
The patent replaces the mechanical human review system with an automated computational system that uses topic modeling and sentiment analysis algorithms. The system assigns topic vectors to comments, determines dominant topics through weight thresholds, calculates sentiment scores, and generates control signals automatically, eliminating both human bias and the impracticality of manual review of large social media datasets.
2Productivity
If machine-based sentiment analysis is implemented, then productivity and consistency are improved, but device complexity increases due to topic modeling and sentiment calculation algorithms
Solution Approach 1:
The patent segments the complex sentiment analysis task into distinct modular components: topic vector assignment, dominant topic determination through weight threshold comparison, sentiment score calculation, and control signal generation. This segmentation reduces system complexity by making each component independent and manageable while maintaining high processing productivity through automated pipeline operation.
3Speed
If automated topic classification and sentiment scoring are used, then analysis speed increases, but measurement precision may be affected by algorithmic limitations
Solution Approach 1:
The patent incorporates feedback mechanisms where sentiment scores are calculated based on dominant topics identified through weight threshold analysis, and control signals are generated when sentiment thresholds are met. This feedback loop allows the system to continuously refine its analysis, improving measurement precision while maintaining high analysis speed through automated processing of topic vectors and sentiment calculations.
Data Source
AI summary
A method for issuing control signals in response to sentiment. In some embodiments, the method includes: assigning, to each of a plurality of comments, a respective topic vector of length k; determining whether a largest element of the topic vector of a first comment of the plurality of comments exceeds a weight threshold; in response to the determining that the largest element exceeds the weight threshold, classifying the first comment into a first topic, of k topics, the first topic corresponding to the position, in the topic vector, of the largest element of the topic vector; calculating a first sentiment score; calculating an average sentiment score, based in part on the first sentiment score; determining whether the average sentiment score meets a criterion; and in response to the determining that the criterion is met, generating a control signal, the control signal including a message related to the first topic.