Sentiment Analysis Using Attention Mechanism and Spatial Transform
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional sentiment analysis methods struggle to capture fine-grained sentiment information in text, resulting in low accuracy in sentiment analysis.
Innovation Solution
A method and apparatus for sentiment analysis that involves obtaining a feature sequence from text, processing it using an attention mechanism to learn context dependency, performing spatial transform to extract and combine features in a multi-layer sentiment space, and recognizing entity attributes to perform sentiment mapping and obtain sentiment polarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sentiment analysis methods are used, then the analysis process is simple, but the accuracy and reliability of sentiment analysis is low
Solution Approach 1:
The patent segments the sentiment analysis process into multiple distinct stages: feature extraction, attention mechanism processing, spatial transform, and sentiment mapping. Each stage handles a specific aspect of the analysis, allowing the system to achieve high accuracy through modular processing while maintaining clarity in the overall architecture.
Solution Approach 2:
The patent introduces a spatial transform dimension that maps attention features into a multi-layer sentiment space. This dimensional transformation enables the system to capture fine-grained sentiment information by representing text in a deeper, more abstract manner, thereby improving measurement precision without simply adding more processing steps.
2Measurement precision
If traditional sentiment analysis methods are used, then the processing speed is fast, but the ability to capture fine-grained sentiment information is limited
Solution Approach 1:
The patent performs preliminary feature extraction and encoding of text before the main sentiment analysis process. By pre-processing the text into feature sequences and encoded features, the system prepares the data in advance, which allows the subsequent attention mechanism and spatial transform operations to proceed more efficiently, thus maintaining processing speed while enabling fine-grained analysis.
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary between the encoded features and the spatial transform layer. This intermediary component selectively processes and weights the encoded features, enabling the system to capture fine-grained sentiment information by focusing on relevant parts of the text without requiring excessive computational resources, thereby preserving processing speed.
3Reliability
If simple text processing is used, then the computational resources required are low, but the context dependency learning is insufficient
Solution Approach 1:
The patent applies local quality by using the attention mechanism to selectively process and weight different parts of the encoded feature sequence. Instead of uniformly processing all features with high computational resources, the attention mechanism identifies and emphasizes locally important features that carry contextual dependency information, thereby improving reliability while optimizing computational resource usage.
Data Source
AI summary
A method for sentiment analysis includes that: a feature sequence corresponding to text is obtained, the feature sequence includes encoded features; each encoded feature in the feature sequence is processed by using an attention mechanism, to obtain an attention feature of the text; the attention feature is transferred to a spatial transform feature of the text; an entity attribute of the spatial transform feature is recognized, and sentiment mapping is performed based on the spatial transform feature to obtain a sentiment polarity of the entity attribute.