Hierarchical Vector Models for Conversation Sentiment Analysis
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Solution Overview
Problem
Current chatbot systems face challenges in analyzing conversations to develop a hierarchical understanding of user interactions, which hinders their ability to accurately determine user sentiment and tailor follow-up interactions effectively.
Innovation Solution
The implementation of hierarchical models that process conversations by generating sentence vectors, modifying them with time values, aggregating these into window vectors with count values, and ultimately producing conversation vectors to determine sentiment, allowing for a deeper understanding of user interactions and sentiment analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hierarchical models with multiple processing layers are implemented to generate conversation vectors, then measurement precision of user sentiment is improved, but device complexity increases
Solution Approach 1:
The conversation processing system is segmented into multiple hierarchical layers: sentence vector generation from word vectors, window vector aggregation from sentence vectors, and conversation vector synthesis from window vectors. Each layer processes data at a different granularity level, enabling precise sentiment analysis through progressive abstraction while maintaining manageable complexity through modular design.
Solution Approach 2:
The system transitions from word-level vectors to sentence-level vectors, then to window-level vectors, and finally to conversation-level vectors, adding temporal and contextual dimensions at each stage. This dimensional progression enables comprehensive sentiment capture by incorporating time windows, sentence semantics, and conversational context simultaneously.
2Reliability
If time values and count values are incorporated into vector processing, then reliability of conversation representation is improved, but loss of time increases
Solution Approach 1:
Time values are pre-incorporated into sentence vectors during the sentence encoding stage, and count values are pre-calculated during window aggregation. This preliminary integration ensures that temporal and frequency information is embedded in the vector representations before final sentiment analysis, improving reliability without requiring additional processing steps at critical decision points.
Solution Approach 2:
The system processes conversations in fixed time windows rather than analyzing the entire conversation sequence at once. By dividing the conversation into manageable temporal segments and processing them partially in parallel, the system achieves comprehensive representation while reducing overall processing time through incremental analysis.
Data Source
AI summary
Systems and methods use hierarchical models to process conversations. A set of word vectors is processed using a sentence model, resulting in a sentence vector for the conversation message. The sentence vector is modified to include a time value. A set of sentence vectors, representing a time window, is processed using a window model, to generate a window vector for that time window. The window vector is updated to include a count value. A set of window vectors, corresponding to a set of time windows within the conversation, is processed using a conversation model to generate a conversation vector. A sentiment value indicating the sentiment of the conversation is presented, using a processor that generates the sentiment value from the conversation vector.