Sentiment Analysis Neural Network Domain Adaptation
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Solution Overview
Problem
Current sentiment analysis methods face challenges in efficiently analyzing unstructured, domain-specific documents at scale, particularly in contexts where limited or dubious data is available, such as in developing markets or for small and medium-sized enterprises, and struggle to maintain context-dependent interpretations of words with varying connotations across different domains.
Innovation Solution
The approach involves representing text data in a multi-dimensional vector space and applying a neural network trained through a two-step process using both non-domain specific and domain-specific training sets, allowing for context-aware sentiment scoring and reducing the need for large domain-specific datasets, while incorporating user feedback for iterative improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sentiment analysis methods are used on domain-specific documents, then analysis can be performed, but accuracy deteriorates due to context-dependent word connotations varying across domains
Solution Approach 1:
The patent applies parameter changes by transforming text data into vector representations in a multi-dimensional space, where semantic relationships are captured through numerical parameters. This allows the system to adapt to different domains by adjusting the vector space parameters rather than requiring complete retraining, thereby maintaining accuracy across domain-specific contexts while improving domain adaptability.
Solution Approach 2:
The patent introduces an intermediary layer between raw text and sentiment classification by using word embeddings and vector representations. This intermediary transforms contextual word meanings into a form that captures domain-specific connotations, allowing the system to maintain high accuracy across different domains without requiring domain-specific training data for each new domain.
2Measurement precision
If large domain-specific training datasets are used to improve accuracy, then sentiment analysis precision improves, but data requirements and system complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on general-purpose text data (such as Wikipedia) before fine-tuning on domain-specific data. This preliminary training establishes a robust foundation of language understanding and semantic relationships, allowing the system to achieve high sentiment analysis accuracy with significantly less domain-specific training data than would be required if starting from scratch.
Solution Approach 2:
The patent merges general-language understanding capabilities with domain-specific sentiment analysis by combining pre-trained language models with domain adaptation techniques. This merging allows the system to leverage large amounts of general text data for foundational training while requiring minimal domain-specific data, thereby achieving high accuracy without proportionally increasing data requirements.
3Reliability
If comprehensive analysis of all documents is performed, then complete sentiment assessment is achieved, but processing time increases
Solution Approach 1:
The patent applies partial action by analyzing representative subsets of documents rather than every single document in a corpus. The system identifies key documents that capture the essential sentiment signals and focuses computational resources on these, achieving reliable sentiment assessment without the time cost of comprehensive analysis of all available documents.
Solution Approach 2:
The patent segments the document analysis process into multiple stages: initial filtering to identify relevant documents, vectorization of key documents, neural network processing of segmented text units, and aggregation of results. This segmentation allows parallel processing and optimization at each stage, maintaining assessment completeness while reducing overall processing time through efficient resource allocation.
Data Source
AI summary
This specification describes methods and systems for sentiment analysis. One of the methods includes: receiving a plurality of documents, each document having text data and for each of the documents: (1) representing at least part of the document's text data in a multi-dimensional vector space to produce vectorized text data; (2) applying a neural network to the vectorized text data to calculate a sentiment score, wherein the neural network has been trained using a two step process including (a) training the neural network with a non-domain specific training set; and (b) training the neural network with a domain specific training set; and (3) determining a sentiment score for an entity based at least in part on the sentiment scores for the plurality of documents.