Neural Hybrid Sentiment Analysis Model Using TF-IDF Vectors
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Solution Overview
Problem
Current sentiment analysis techniques in social media data, such as those used in web and mobile applications, often fail to accurately capture the nuances of user sentiments due to their reliance on static word scores and dictionaries, leading to inaccurate reflections of user satisfaction and opinions.
Innovation Solution
A machine-trained system that extracts text documents from social networks, tokenizes them, converts words into vectors, applies TF-IDF to generate sentence vectors, randomly selects subsets for sentiment labeling, trains a classifier, and predicts sentiments, thereby improving the accuracy of sentiment analysis by capturing linguistic regularities and distinguishing between semantically different sentences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static word scores and dictionaries are used for sentiment analysis, then the analysis process is simple and fast, but the accuracy of sentiment classification deteriorates
Solution Approach 1:
The patent transitions from static word scores to dynamic contextual analysis by using neural networks that adaptively weigh words based on their context in sentences and documents. The system dynamically adjusts sentiment scores based on surrounding words, sentence structure, and document-specific patterns, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The patent changes the parameters of sentiment analysis from fixed dictionary scores to variable contextual features. By extracting multiple features (word-level, sentence-level, document-level) and using machine learning to weight them dynamically, the system achieves higher accuracy while maintaining computational efficiency through feature engineering.
2Measurement precision
If contextual analysis and machine learning are used to improve sentiment accuracy, then classification precision improves, but system complexity increases
Solution Approach 1:
The patent segments the sentiment analysis process into distinct hierarchical levels: word-level features, sentence-level features, and document-level features. Each level extracts specific characteristics independently, and the results are combined through machine learning. This segmentation manages complexity by breaking down the overall task into smaller, more manageable components.
Solution Approach 2:
The patent creates a multi-functional system where the same machine learning framework processes multiple types of features (linguistic, contextual, structural) across different levels of analysis. The unified approach handles various sentiment expressions consistently, reducing overall system complexity despite the sophisticated analysis performed.
Data Source
AI summary
Machine training for determining sentiments in social network communications. A text document is extracted from a web site and tokenized into tokens. The tokens are input to a word to vector conversion model to generate word vectors. A term frequency inverse document frequency (TF-IDF) algorithm converts the word vectors to sentence vectors. A randomly selected subset the sentence vectors are tagged and used to train a classifier. The classifier takes a sentence vector and predicts a sentiment associated with the sentence vector. Predicted sentiment associated with each of the sentence vectors may be combined to generate a sentiment associated with the text document.