Neural Network Sentiment Analysis with Contextual Attributes
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Solution Overview
Problem
Current sentiment analysis methods fail to effectively incorporate contextual data from conversation channels, leading to incomplete or inaccurate sentiment scoring in various communication platforms.
Innovation Solution
An artificial neural network (ANN) is employed to determine conversation snippet sentiment scores by integrating content and contextual attributes, such as user roles and project phases, using document matrices and context vectors, and applying hidden layer sequences and a softmax output layer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sentiment analysis methods are used, then the analysis process is simple, but the sentiment scoring accuracy is incomplete or inaccurate due to failure to incorporate contextual data
Solution Approach 1:
The patent segments the sentiment analysis system into distinct functional modules: a neural network component for processing content and contextual attributes separately, and a score weighing component for aggregating multiple sentiment scores. This segmentation allows the system to handle complex contextual data while maintaining manageable system architecture and improving measurement precision through specialized processing for each data type.
Solution Approach 2:
The patent transitions from traditional single-dimension sentiment analysis to multi-dimensional analysis by incorporating contextual attributes (user role, project phase, conversation channel) as additional dimensions. The neural network processes both content features and contextual features in parallel, creating a higher-dimensional sentiment score that captures nuanced meanings across multiple dimensions simultaneously.
2Adaptability or versatility
If contextual data from multiple conversation channels is incorporated, then the comprehensiveness of sentiment analysis is improved, but the data processing complexity increases
Solution Approach 1:
The patent implements a universal neural network architecture that can process multiple types of conversation channels (social media, instant messaging, email, project management applications) through the same processing pipeline. The system uses channel-specific embedding layers that adapt to each conversation type while maintaining a unified processing framework, enabling multi-functionality without proportionally increasing processing complexity.
Solution Approach 2:
The patent introduces contextual attribute embeddings as intermediary representations that bridge raw contextual data and sentiment scoring. These embeddings serve as mediators that transform diverse contextual attributes (user roles, project phases, conversation channels) into a standardized format that the neural network can process efficiently, reducing the complexity of handling heterogeneous data sources.
3Reliability
If multiple sentiment scores are aggregated using score weighing, then the overall sentiment accuracy is improved, but the computational requirements increase
Solution Approach 1:
The patent dynamically adjusts weighting parameters for different sentiment scores based on contextual attributes such as user role, project phase, and conversation channel reliability. Instead of using fixed weights, the system modifies weighting parameters adaptively, allowing more reliable sources to have greater influence while reducing computational resources spent on less reliable inputs, thus improving overall accuracy efficiently.
Data Source
AI summary
An artificial neural network (ANN) determines a conversation snippet sentiment score based on content of the conversation snippet and contextual attributes associated with the conversation snippet. Contextual attributes may include, for example, a role within an organizational hierarchy of a user participating in the conversation snippet. Information representing the content is input into a hidden layer sequence of the ANN; information representing the contextual attributes is input into another hidden layer sequence of the ANN. Additionally or alternatively, a weighing engine determines a topical sentiment score by aggregating weighted conversation snippet sentiment scores. Weights to be applied to the conversation snippet sentiment scores may be determined based on, for example, a proportion of conversation snippets associated with the same topic that are conducted on a particular conversation channel as compared with other conversation channels, and respective roles of users participating in the conversation snippets conducted on the particular conversation channel.