Automated Content Summarization with Event-Based Analysis
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Solution Overview
Problem
The rapid growth of data volume related to brand perception and sentiment analysis makes manual monitoring and summarization of content from various media sources impractical, leading to delayed insights and human error.
Innovation Solution
An automated system using Hierarchical Dirichlet Processes (HDP) and Latent Dirichlet Allocation (LDA) for topic modeling, combined with Restricted Boltzmann Machines (RBM), to analyze and summarize content in real-time, identifying key themes and sentiments across multiple data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring and summarization of content is used, then human understanding and interpretation of sentiments can be achieved, but the process becomes slow and impractical due to massive data volume
Solution Approach 1:
The patent replaces manual mechanical monitoring and summarization processes with automated computational systems including natural language processing algorithms, machine learning models, and data analytics platforms. These systems automatically ingest, analyze, and summarize content from multiple media sources, eliminating the bottleneck of human processing while maintaining or improving sentiment analysis accuracy through consistent, scalable computational methods.
2Reliability
If manual handling of large volumes of data is performed, then detailed analysis can be conducted, but human error increases and response time to immediate events is delayed
Solution Approach 1:
The patent implements continuous automated monitoring and analysis systems that operate without interruption, continuously ingesting data from multiple media sources and providing real-time or near-real-time analysis. This continuous automated process eliminates gaps in monitoring, ensures consistent application of analysis criteria, and provides immediate responses to emerging events without the delays and errors associated with manual batch processing.
3Adaptability or versatility
If the number of topics is predetermined in topic modeling, then the modeling process is simpler, but it cannot adapt to organic topic discovery and changes over time
Solution Approach 1:
The patent employs dynamic topic modeling approaches where the number and nature of topics are not fixed in advance but emerge and evolve based on the data being analyzed. The system can automatically discover new topics, merge similar topics, and adapt topic structures over time to reflect changing conversations and media landscapes, providing flexibility and adaptability while managing complexity through automated topic lifecycle management.
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.