Domain-Independent Sentiment Analysis via Adjective Polarity Masking
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Solution Overview
Problem
Current natural language processing techniques struggle to effectively determine sentiment in user reviews across different domains due to resource-intensive rule-based approaches and the need for domain-dependent training of deep learning models, which can be time-consuming and impractical for emerging domains with limited data.
Innovation Solution
A method and system that utilize an adjective-polarity database to associate polarity values with adjectives in natural language text content, allowing for the training of a domain-independent aspect-based sentiment analysis model by masking nouns and adjectives with part-of-speech tags, enabling sentiment scoring across various domains without requiring new model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are trained on domain-dependent corpora for aspect-based sentiment analysis, then sentiment analysis accuracy is improved, but model training time and resource requirements increase
Solution Approach 1:
The patent creates a universal sentiment analysis model that can operate across multiple domains without requiring separate domain-specific training. The model uses a combination of pre-trained language models and transfer learning techniques to achieve domain-independent sentiment analysis, allowing a single model to serve multiple purposes across different product categories and review types.
Solution Approach 2:
The patent employs parameter adaptation techniques where the model learns from diverse domains and adjusts its internal parameters to generalize across different domains. By changing the training approach from domain-specific to domain-independent parameter learning, the model achieves both accuracy and efficiency without requiring retraining for each new domain.
2Device complexity
If rule-based approaches are used for sentiment analysis, then implementation is simpler, but resource consumption and complexity increase
Solution Approach 1:
The patent replaces traditional rule-based mechanical systems with neural network-based computational models. Instead of using hand-crafted rules that consume significant computational resources, the system uses pre-trained language models that process text through learned representations, achieving both simplicity in implementation and efficiency in resource consumption.
3Device complexity
If traditional star rating based processing is used, then analysis is simpler, but information loss occurs
Solution Approach 1:
The patent extracts meaningful sentiment information from the full text content rather than relying solely on star ratings. By processing the actual text using natural language understanding models, the system recovers and analyzes the nuanced information contained in review text, capturing sentiment nuances that star ratings alone cannot convey.
Data Source
AI summary
A system and method for determining sentiment of natural language text content in a domain independent manner is provided. The method comprises providing an adjective-polarity database having stored therein a list of adjectives and corresponding polarity values. The method further comprises receiving natural language text content related to a first domain with information about corresponding sentiment. The method further comprises identifying nouns and adjectives in the received tagged natural language text content. The method further comprises associating the polarity value to each of the adjectives identified in the received tagged natural language text content. The method further comprises masking the identified nouns and adjectives in the received tagged natural language text content with part-of-speech tags. The method further comprises utilizing the masked natural language text content for training of a model for determining a sentiment score for natural language text content related to a second domain.