Sentiment Classifier Using Nearest-Neighbor Lookup
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Solution Overview
Problem
Current automatic sentiment classification methods are costly and resource-intensive, often resulting in poor results, and human classification is time-consuming and expensive, especially when dealing with large volumes of similar textual passages like Tweets or Facebook comments.
Innovation Solution
A passage sentiment classifier that uses a nearest-neighbor classifier to automatically assign a score indicating the polarity and magnitude of sentiment by locating similar passages in a storage system that have been manually classified, allowing for efficient classification of similar texts using similarity search and string distance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification by experts is used, then classification accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The system performs preliminary manual classification on a subset of representative passages to create a training dataset, then uses this pre-prepared data to train an automatic classifier that can rapidly classify remaining passages without requiring continuous human intervention
Solution Approach 2:
The system creates copies of manually classified passages and uses them as training examples to teach the automatic classifier, allowing the system to learn from human expertise without requiring humans to classify every single passage
2Productivity
If automatic classification methods are used, then speed and cost are reduced, but classification accuracy deteriorates
Solution Approach 1:
The system introduces a training dataset as an intermediary between manual classification and automatic classification, allowing the automatic classifier to learn from human-labeled examples and improve its accuracy while maintaining high processing speed
Solution Approach 2:
The system changes the parameter of classification approach from purely automatic or purely manual to a hybrid approach that combines automatic processing with learned patterns from manual examples, optimizing both speed and accuracy
3Reliability
If extensive manual classification is performed, then training data quality is improved, but resource cost increases
Solution Approach 1:
The system performs manual classification on only a partial subset of passages that are most representative or informative for training, rather than manually classifying all passages, thereby achieving sufficient training data quality at reduced cost
Data Source
AI summary
A passage sentiment classifier that can be used to assign a score that indicates the polarity and magnitude of sentiment expressed by a piece of text using information about similar passages. A passage of text may be a few words, a sentence, a paragraph or an entire document. The invention described classifies automatically passages by first looking up the most similarly classified passage in a storage system, which contains passages that have been classified manually by a human.