Automated Content Summarization Using LDA and RBM Algorithms
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Solution Overview
Problem
The manual monitoring of vast amounts of data related to brand perception from various media sources is inefficient and prone to human error, making it difficult to provide timely and accurate summaries of key topics and sentiments.
Innovation Solution
An automated system using Latent Dirichlet Allocation (LDA) and Restricted Boltzmann Machines (RBM) algorithms to analyze and summarize content from multiple data sources, identifying peaks in data volume and extracting representative stories and themes, which are then customized based on user profiles for real-time delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring of data is used, then human understanding and interpretation of sentiments is improved, but the speed and timeliness of analysis deteriorates
Solution Approach 1:
The patent introduces an automated processing system with machine learning algorithms as an intermediary between raw data and human analysts. The system pre-processes, filters, and structures vast amounts of data, presenting refined insights to human users who then focus on high-level interpretation rather than manual data processing.
Solution Approach 2:
The patent replaces manual mechanical data processing with automated computational systems. Machine learning models and natural language processing algorithms substitute human manual analysis for tasks like data collection, cleaning, initial sentiment classification, and pattern recognition, enabling both speed and accuracy.
2Quantity of substance
If the volume of data to be monitored increases, then the comprehensiveness of brand perception analysis is improved, but the difficulty of manual handling increases
Solution Approach 1:
The patent segments the overwhelming volume of data into manageable categories and components using automated classification systems. Data is divided by source, topic, sentiment type, and relevance, allowing the system to process each segment independently and efficiently while maintaining comprehensive coverage.
Solution Approach 2:
The patent creates a multi-functional automated processing platform that handles diverse data types (social media posts, news articles, reviews, etc.) from multiple sources simultaneously. The system performs collection, cleaning, classification, sentiment analysis, and visualization through a single integrated platform, reducing overall complexity.
3Productivity
If automated processing is implemented, then the speed of analysis is improved, but the need for algorithmic complexity increases
Solution Approach 1:
The patent implements a nested architecture where simple data collection modules feed into progressively more complex processing layers. Basic filtering occurs at the first level, followed by topic modeling, then sentiment analysis, and finally high-level insight generation. Each layer builds on the previous one, managing complexity through hierarchical organization.
Solution Approach 2:
The patent performs preliminary data processing actions automatically before main analysis begins. Data is pre-collected, pre-filtered for relevance, pre-cleaned of noise, and pre-categorized into topics during off-peak times or in parallel streams, reducing the computational burden during real-time analysis and improving overall productivity.
Data Source
AI summary
Embodiments disclose a method for automatic summarization of content. The method includes accessing a plurality of stories from a plurality of data sources for a predefined time. Each story is associated with a media item. The method includes plotting the plurality of stories over the predefined time for determining one or more peaks and extracting a set of stories from the one or more peaks. The method includes detecting one or more themes from the set of stories using LDA algorithm. Each theme is associated with a group of stories. The method further includes determining at least one subset of stories for each theme from the group of stories representing the set of stories in the one or more peaks using RBM algorithm. The method includes generating a summarized content for each user based on an associated user profile and the at least one subset of stories.