Semantic Analysis of Song Lyrics Using Story Graphs
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Solution Overview
Problem
Traditional media content delivery systems lack semantic understanding of media content and lyrics, failing to provide personalized recommendations based on meaningful information within songs.
Innovation Solution
A system and method for semantic analysis of song lyrics, generating story graphs to determine feature vectors, which match input vectors against song features for personalized content selection and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional media content delivery systems are used, then content delivery is simple and straightforward, but semantic understanding of media content and lyrics is lacking
Solution Approach 1:
The system segments the lyrics text into meaningful units (words, phrases, sentences) and processes each segment through NLP operations to extract semantic features. This segmentation allows the system to handle complex semantic analysis by breaking it down into manageable processing steps, thereby improving semantic understanding without overwhelming system complexity
Solution Approach 2:
The patent introduces an intermediary semantic analysis layer that sits between the raw lyrics and the recommendation engine. This intermediary layer generates story graphs and feature vectors that bridge the gap between unstructured text and structured recommendation criteria, enabling semantic understanding while maintaining system modularity and manageable complexity
2Adaptability or versatility
If semantic analysis is implemented to provide personalized recommendations, then recommendation quality improves, but processing complexity increases
Solution Approach 1:
The system performs preliminary semantic analysis on all lyrics in advance, generating story graphs and feature vectors before actual recommendation requests are made. This pre-processing approach allows the system to have complex semantic understanding capabilities ready and available, so that when a recommendation request comes in, the actual processing is simpler and faster
Solution Approach 2:
The patent transforms unstructured lyrical text into structured parameter representations (feature vectors with specific dimensions representing different semantic aspects). This parameter transformation enables the system to handle personalization requests by comparing and matching parameters rather than processing raw text, reducing processing complexity while maintaining adaptability
3Measurement precision
If detailed semantic analysis is performed on all songs, then recommendation accuracy improves, but processing time increases
Solution Approach 1:
The system performs detailed semantic analysis and generates comprehensive feature vectors for all songs in advance, storing these pre-computed representations. When a recommendation request is received, the system only needs to perform fast vector similarity comparisons rather than re-running the full semantic analysis, thereby maintaining high recommendation accuracy while significantly reducing real-time processing time
Solution Approach 2:
The patent creates simplified copies of the semantic content in the form of feature vectors that capture the essential semantic characteristics without containing the full complexity of the original lyrical analysis. These vector copies enable fast comparison and matching operations while preserving the semantic information needed for accurate recommendations
Data Source
AI summary
In accordance with an embodiment, described herein is a system and method for semantic analysis and use of song lyrics in a media content environment. Semantic analysis is used to identify persons, events, themes, stories, or other meaningful information within a plurality of songs. For each song, a story graph is generated which describes a narrative within that song's lyrics. The story graph is then used to determine a feature vector associated with the song's narrative. In response to receiving an input vector, for example as a search input for a particular song track, the input vector can be matched against feature vectors of the plurality of songs, to determine appropriate tracks. Example use cases include the selection and delivery of media content in response to input searches for songs of a particular nature, or the recommendation or suggestion of media content in social messaging or other environments.