Sentiment Classifier Taxonomy for NLP Accuracy
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Solution Overview
Problem
Existing sentiment analysis methods using natural language processing are limited in accuracy and efficiency due to the dynamic nature of word meanings and sentiment classification, particularly when dealing with linguistic categories and negation/inversion elements.
Innovation Solution
The development of a system that extracts features from training data using a combination of feature extraction, value determination, and supervised classification techniques, such as support vector machines, to generate sentiment classifiers, which can accurately assess sentiment in documents by labeling units as positive, negative, or neutral, and organizing these classifiers into taxonomies for efficient analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated sentiment analysis is performed using data mining techniques such as bag of words and support vector machines, then the analysis can be automated, but the accuracy is limited due to the dynamic nature of word meanings and linguistic categories
Solution Approach 1:
The patent segments the text analysis process into multiple feature extraction components that operate at different levels: word level, phrase level, and sentence level. Each component extracts specific features (e.g., sentiment words, negation markers, linguistic categories) that are then combined to form a comprehensive sentiment classification, thereby improving accuracy while maintaining automation.
Solution Approach 2:
The patent changes the parameters of analysis by extracting multiple types of features beyond simple word counts, including sentiment intensity, linguistic category, negation status, and contextual relationships. These multi-dimensional parameters enable more accurate sentiment classification while the entire process remains automated through supervised learning algorithms.
2Measurement precision
If multiple sentiment classifiers are provided for different subjects and organized into a taxonomy, then the accuracy for specific subjects is improved, but the device complexity increases
Solution Approach 1:
The patent creates a universal taxonomy structure where sentiment classifiers are organized hierarchically by subject matter. The same feature extraction and classification mechanisms are reused across different subject domains, with the taxonomy providing a systematic way to select appropriate classifiers. This multi-functional approach improves accuracy for specific subjects while avoiding redundant complexity through shared underlying components.
3Measurement precision
If feature extraction is performed to capture linguistic categories and contextual elements, then the accuracy of sentiment classification is improved, but the processing time and complexity increase
Solution Approach 1:
The patent performs preliminary feature extraction during the training phase, where the system learns to identify and extract relevant features (sentiment words, negation markers, linguistic categories) from labeled training data. This preliminary action creates a robust feature extraction framework that can be efficiently applied to new data, improving accuracy while managing complexity through pre-learned patterns.
Data Source
AI summary
Method and apparatus are provided for providing one or more sentiment classifiers from training data using supervised classification techniques based on features extracted from the training data. Training data includes a plurality of units such as, but not limited to, documents, paragraphs, sentences, and clauses. A feature extraction component extracts a plurality of features from the training data, and a feature value determination component determines a value for each extracted feature based on a frequency at which each feature occurs in the training data. On the other hand, a class labeling component labels each unit of the training data according to a plurality of sentiment classes to provide labeled training data. Thereafter, a sentiment classifier generation component provides a least one sentiment classifier based on the value of each extracted feature and the labeled training data using a supervised classification technique.