Sentiment Analysis via Attention-Fused CNN and Bi-LSTM Features
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Solution Overview
Problem
Existing sentiment analysis technologies face limitations due to their reliance on single features and models, which fail to effectively capture the complexity of emotional information in text, leading to incomplete extraction of emotional data and reduced accuracy in emotion classification.
Innovation Solution
A semantic sentiment analysis method that preprocesses text into a uniformly formatted matrix of word vectors, extracts local and contextual semantic emotional features using convolutional neural networks and bidirectional long short-term memory networks, and employs an attention mechanism to fuse these features, followed by a softmax classifier for final analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional language features and machine learning methods are used for sentiment analysis, then the system is simple and easy to implement, but the extraction of emotional information is incomplete and accuracy is limited
Solution Approach 1:
The patent combines multiple deep learning models (CNN for local feature extraction, Bi-LSTM for temporal sequence modeling, and attention mechanism for feature fusion) into a unified sentiment analysis system. This merging of multiple specialized components resolves the contradiction by achieving comprehensive emotional information extraction through collaborative processing, while maintaining systematic organization that prevents excessive complexity
Solution Approach 2:
The patent segments the sentiment analysis task into distinct functional components: local semantic feature extraction via CNN, contextual temporal modeling via Bi-LSTM, and feature fusion via attention mechanism. This segmentation allows each component to specialize in specific aspects of emotional information processing, improving overall accuracy without creating an undifferentiated complex system
2Measurement precision
If deep learning focuses on a single model and single feature, then the system complexity is low, but the extraction of emotional information is insufficient and sentiment analysis effect is limited
Solution Approach 1:
The patent introduces multiple dimensional perspectives for feature extraction: spatial local features through CNN filters, temporal sequence features through Bi-LSTM layers, and weighted fused features through attention mechanism. This multi-dimensional approach resolves the contradiction by capturing emotional information from different aspects simultaneously, achieving comprehensive extraction without relying on a single complex feature engineering process
3Measurement precision
If multiple features and models are fused for sentiment analysis, then the extraction of emotional information is improved, but the system complexity increases
Solution Approach 1:
The attention mechanism serves as an intermediary component that selectively weights and fuses features from CNN and Bi-LSTM models. This intermediary resolves the complexity issue by providing a systematic method for feature integration, automatically determining the importance of different features without requiring manual complex feature engineering, thus improving extraction effectiveness while maintaining manageable system complexity
Data Source
AI summary
Disclosed is a semantic sentiment analysis method fusing in-depth features and time sequence models, including: converting a text into a uniformly formatted matrix of word vectors; extracting local semantic emotional text features and contextual semantic emotional text features from the matrix of word vectors; weighting the local semantic emotional text features and the contextual semantic emotional text features by using an attention mechanism to generate fused semantic emotional text features; connecting the local semantic emotional text features, the contextual semantic emotional text features and the fused semantic emotional text features to generate global semantic emotional text features; and performing final text emotional semantic analysis and recognition by using a softmax classifier and taking the global semantic emotional text features as input.