Reverse Sentence Reconstruction for Text Sentiment Analysis
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Solution Overview
Problem
Existing methods for detecting human feelings in text, such as bag-of-words models, face challenges with negation, sarcasm, complex sentence structures, and context-dependent word meanings, leading to inaccurate classifications and failure to scale for large volumes of social media content.
Innovation Solution
The implementation of a system that uses a reverse sentence reconstruction (RSR) utility and sentence vectorization technique (SVT) to analyze text data, generating syntactic text trees and computing feeling classifications, which accounts for grammatical and semantic structures, and includes a slang and spelling dictionary for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If bag-of-words model is used for feeling detection, then implementation is simple, but accuracy deteriorates due to inability to handle negation, sarcasm, and context-dependent meanings
Solution Approach 1:
The patent segments text into syntactic structures (phrases, clauses, sentences) and analyzes feeling expressions within each segment contextually. The reverse sentence reconstruction divides text into meaningful units that preserve grammatical relationships, allowing accurate detection of negation and sarcasm while maintaining manageable analysis complexity.
Solution Approach 2:
The patent introduces reverse sentence reconstruction as an intermediary step between raw text and feeling classification. This intermediary process transforms text into a structured format that preserves contextual information, enabling accurate feeling detection without requiring complex direct analysis of the original text.
2Productivity
If traditional keyword matching is used, then processing speed is fast, but accuracy deteriorates due to failure to detect sarcasm and complex sentence structures
Solution Approach 1:
The patent performs reverse sentence reconstruction and syntactic analysis before feeling classification. This preliminary action organizes text into structured formats that preserve contextual information, enabling subsequent fast processing of pre-analyzed data rather than requiring slow, complex analysis during classification.
Solution Approach 2:
The patent uses dynamic programming to identify feeling expressions within syntactic structures. This dynamic approach adapts to varying sentence structures and contexts, maintaining high processing speed while accurately detecting sarcasm and complex grammatical patterns through flexible, context-aware analysis.
3Measurement precision
If context-aware analysis is implemented, then accuracy improves, but device complexity increases due to need for grammatical and semantic processing
Solution Approach 1:
The patent uses reverse sentence reconstruction, which inverts the normal analysis approach by building understanding from sentence level down to phrase and word levels. This inversion simplifies complexity management by establishing a clear hierarchical structure that systematically processes contextual information without requiring complex simultaneous analysis of all text elements.
Solution Approach 2:
The patent adds a syntactic structure dimension to the analysis, organizing text into hierarchical layers (sentences, phrases, words) with defined grammatical relationships. This dimensional organization transforms complex contextual analysis into structured, multi-level processing that improves accuracy while managing system complexity through systematic decomposition.
4Adaptability or versatility
If comprehensive syntactic analysis is used, then scalability improves for large volumes of text, but processing complexity increases
Solution Approach 1:
The patent segments large volumes of text into independent syntactic units (sentences, phrases) that can be processed separately and efficiently. This segmentation enables parallel processing and scalable analysis of large text corpora while maintaining comprehensive syntactic analysis through systematic, modular processing of each unit.
Solution Approach 2:
The patent applies syntactic analysis selectively to portions of text that contain feeling expressions rather than analyzing every word uniformly. This partial action approach maintains scalability by focusing computational resources on relevant segments while preserving comprehensive analysis where needed, balancing complexity and versatility.
Data Source
AI summary
Embodiments described herein relate generally to content analysis technologies and natural language processing (NLP). In particular, devices, systems, and methods may implement a reverse sentence reconstruct (RSR) utility, and a sentence vectorization technique (SVT) utility. A computer server may be configured to receive a feeling classification request with text data elements, and in response, generate a feeling classification response indicating feeling for the text data elements using the RSR utility and the SVT utility.