Multi-Topic Sentiment Analysis via Post-Topic Pair Mapping
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Solution Overview
Problem
Current sentiment analysis methods struggle to effectively and efficiently identify multiple topics and their corresponding sentiment polarities in documents, particularly in online discussions where topics diverge and evolve over time, limiting their applicability in applications like marketing and customer service.
Innovation Solution
A computer-implemented framework that preprocesses documents to form vectors, applies topic modeling to extract topics, maps posts to topics, and analyzes these pairs to determine user sentiments, using techniques like Latent Dirichlet Allocation and natural language processing to identify sentiment polarities and relationships within documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If topic modeling is applied to extract topics from posts, then topic extraction capability is improved, but computational complexity and processing time increase
Solution Approach 1:
The system segments the document analysis process into distinct stages: preprocessing individual posts to form vectors, applying topic modeling to extract topics, mapping posts to topics to form pairs, and analyzing pairs to determine sentiments. This segmentation allows each stage to be optimized independently, reducing overall computational complexity while maintaining topic extraction accuracy.
Solution Approach 2:
The system performs preliminary preprocessing of posts to form vectors before applying topic modeling. This preliminary action prepares the data in advance, making the subsequent topic extraction process more efficient and less computationally intensive, thereby resolving the contradiction between extraction accuracy and computational complexity.
2Loss of information
If sentiment analysis is performed on multiple topics in documents, then analysis comprehensiveness is improved, but processing efficiency decreases
Solution Approach 1:
The system segments the sentiment analysis process by first extracting multiple topics from posts, then mapping posts to specific topics to form pairs, and finally analyzing each post-topic pair to determine sentiments. This segmentation enables comprehensive multi-topic sentiment analysis while improving efficiency by focusing analysis on specific topic-post relationships rather than analyzing all content uniformly.
Solution Approach 2:
The system introduces a new dimension by creating post-topic pairs, transforming the analysis from a flat document-level sentiment analysis to a structured two-dimensional analysis. This dimensional change allows comprehensive coverage of multiple topics while maintaining processing efficiency through the organized pair-based analysis approach.
3Measurement precision
If posts are pre-processed to form vectors and mapped to topics, then analysis accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary preprocessing of posts to form vectors and establishes topic mappings in advance before conducting sentiment analysis. These preliminary actions organize the data structure and create the post-topic pair framework beforehand, which improves subsequent analysis accuracy while reducing actual processing time during the sentiment determination phase.
Data Source
AI summary
A system and method for analyzing documents, such as posts, on-line reviews and comments from people based on topics of the documents, to determine general sentiment of users is disclosed. Topics from the documents and their corresponding sentiment polarities are extracted. The documents are regarded to be constituted by a series of topics. The sentiment for a topic is represented by a quadruple (k, so, h, i), where k is the topic, so is the sentiment opinion, h is the comment or post holder, and i is the document. A quintuple (k, sup, p, n, ne) is used to illustrate the topics and corresponding sentiments and is stored in S, where sup indicates the frequency of the topic, and p (positive), n (negative) and ne (neutral) are different types of opinions of the users. From the quintuple set S, every topic is related to three kinds of sentiment opinions (positive, negative, and neutral), enabling determination of popular topics in documents as well as the users' sentiment polarities.