Sentiment Classification Using Out of Domain Data Identifiers
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Solution Overview
Problem
Existing methods for sentiment classification of opinion data are inefficient and costly, particularly when dealing with large volumes of data from diverse sources, as they require manual human categorization or author-provided category information, which is not always available.
Innovation Solution
The use of classifiers that match a trained source domain to a target domain, employing common and domain-specific identifiers to predict sentiment, with the ability to adjust weights and remove inaccurate identifiers, allowing for automated sentiment classification across different domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual human categorization is used to classify opinion data, then classification accuracy can be maintained, but time consumption and cost increase significantly
Solution Approach 1:
The patent creates a target classifier that copies the identifier structure and sentiment prediction capabilities from a trained source domain classifier. This allows the target classifier to inherit accurate sentiment classification without requiring manual human categorization, thus maintaining classification accuracy while eliminating time-consuming manual work.
Solution Approach 2:
The patent adjusts the weights of identifiers when transferring from source domain to target domain. By changing the weight parameters of identifiers based on their relevance to the target domain, the system adapts the classification model to maintain accuracy across different domains without manual re-categorization.
2Ease of operation
If author-provided category information is required for sentiment classification, then classification can be performed, but the method becomes inapplicable to many opinion data sources where such information is unavailable
Solution Approach 1:
The patent develops a universal sentiment classification approach that works across multiple domains without requiring domain-specific author-provided category information. The target classifier uses identifiers that are applicable to various opinion data sources including forums, blogs, and customer reviews, making the system universally applicable.
Solution Approach 2:
The patent introduces identifiers as intermediary elements that bridge the gap between raw opinion data and sentiment classification. These identifiers serve as mediators that capture sentiment information without requiring direct author-provided category labels, enabling classification of data from diverse sources.
3Measurement precision
If domain-specific classifiers are trained separately for each domain, then classification accuracy for that domain improves, but the complexity and resource requirements increase
Solution Approach 1:
The patent merges the functionality of multiple domain-specific classifiers into a single target classifier framework. By combining the identifier-based approach from source domains with target domain data, the system achieves domain-specific accuracy without maintaining separate classifiers for each domain, thus reducing system complexity.
Solution Approach 2:
The patent performs preliminary training in a source domain to develop identifier-based sentiment prediction capabilities before applying them to the target domain. This preliminary action allows the target classifier to start with pre-learned sentiment patterns, reducing the need for extensive domain-specific training and simplifying the overall system.
Data Source
AI summary
Providing sentiment classification of out of domain data are disclosed herein. In some aspects, a source domain having a trained classifier is matched to a target domain having a target classifier. The trained classifier may include identifiers that may be used to predict the sentiment of opinion data for the source domain. The target classifier may use the identifiers of the trained classifier to determine the sentiment of opinion data for the target domain.