LLM Sentiment Analysis for Temporal Review and Comment Influence
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Solution Overview
Problem
The task of identifying, collecting, analyzing, and interpreting vast amounts of multimedia-related content for sentiment analysis is complex, time-consuming, and prone to human biases and inconsistencies, leading to inaccurate summaries.
Innovation Solution
A sentiment analysis system utilizing large language models (LLMs) and vision language models (VLMs) processes data from diverse sources, including video and text, to generate summaries and analyze user comments, tracking temporal changes in sentiment and user influence, with data stored in a spatial-temporal database for comprehensive querying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human analysts manually collect and analyze vast amounts of multimedia content, then detailed insights can be obtained, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces manual human analysis with automated machine learning models that process multimedia content. The system uses trained models to automatically analyze video comments, text reviews, and other content sources, eliminating the time-consuming manual collection and analysis process while maintaining comprehensive coverage of large datasets
Solution Approach 2:
The system creates digital copies and representations of multimedia content for analysis. By transcribing video comments to text, extracting key information, and storing processed data in databases, the system enables rapid repeated analysis without requiring continuous manual intervention, thus reducing time loss while maintaining analysis accuracy
2Measurement precision
If human analysts manually interpret content, then contextual understanding is achieved, but biases and inconsistencies arise
Solution Approach 1:
The system transforms subjective human interpretation into objective measurable parameters. By converting sentiment analysis into quantifiable metrics (positive/negative/neutral classifications, sentiment scores, topic tags), the system eliminates human biases and ensures consistent, reproducible results across different analysts and time periods
Solution Approach 2:
The system incorporates feedback mechanisms where analysis results are continuously refined. The machine learning models learn from labeled data and can be retrained on new information, allowing the system to correct and improve its interpretations over time, thereby enhancing reliability and reducing inconsistencies in sentiment analysis
3Quantity of substance
If comprehensive data collection from multiple sources is performed, then thorough analysis is achieved, but system complexity increases
Solution Approach 1:
The system divides the complex task of analyzing vast multimedia data into manageable segments. By separating data collection, processing, analysis, and storage into distinct modules handled by specialized machine learning models, the system can process large volumes of data from multiple sources without overwhelming complexity in any single component
Solution Approach 2:
The system employs universal machine learning frameworks that can handle multiple types of content (video, audio, text) through a single integrated architecture. The same processing pipeline can analyze different formats by converting them to standardized representations, reducing system complexity compared to separate specialized systems for each content type
Data Source
AI summary
Approaches presented herein relate to performing of sentiment analysis on various types of content, such as product reviews. The resulting sentiment data can be provided for various uses, such as to allow for sentiment-based search or to make sentiment-based recommendations. An example system collects and processes product reviews from various sources. A first language model (such as an LLM or VLM) may be used to analyze a review to generate a summary and perform sentiment analysis. A second language model may be used to perform further analysis based on the sentiment data to infer correlation between comments and reviews and an influence of the review and the comments. Such an approach can analyze the influence of user comments on subsequent reviews from the same commentator, top concerns and issues highlighted by the commentator, inherent bias, features evaluated, etc. The generated sentiment data and associated timestamp data can be stored and indexed in a database for subsequent retrieval or analysis.