Social Network Message Categorization via Adaptive TF-IDF Scoring
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Solution Overview
Problem
Current text classification systems, particularly in social network message categorization, face challenges in efficiently identifying relevant information from short messages due to the brevity and ambiguity of user-generated content, which traditional techniques like TF-IDF struggle to handle effectively.
Innovation Solution
A method for scoring and categorizing social network messages based on text term frequency calculations, using a message server to match messages with query terms, and returning messages with scores exceeding a threshold, while accounting for unique keywords and terminology across different messaging services, including user tagging and third-party database integration for keyword identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text classification techniques like TF-IDF are used, then they work well for large documents, but they fail to effectively categorize short social network messages
Solution Approach 1:
The patent changes the parameters of text classification by adapting TF-IDF weighting specifically for short messages, modifying how term frequency and inverse document frequency are calculated to account for the brevity constraint, thereby maintaining categorization accuracy while adapting to short message format
Solution Approach 2:
The patent segments the categorization process into multiple stages: initial filtering using modified TF-IDF, followed by contextual analysis, and final ranking. This segmentation allows the system to handle short messages effectively by breaking down the classification task into manageable steps that account for message brevity
2Ease of manufacture
If regular expression matching is used for searching short messages, then it is simple to implement, but it fails when terms are ambiguous or used in unrelated topics
Solution Approach 1:
The patent introduces an intermediary layer between simple regex matching and final categorization. This intermediary performs contextual analysis and term disambiguation, using the matched terms as inputs to a more sophisticated classification process that resolves ambiguity while building upon the simple matching foundation
Solution Approach 2:
The patent performs preliminary action by pre-processing messages to identify and weight key terms before the actual search and matching process. This preliminary term extraction and weighting improves search accuracy by focusing on the most relevant terms while maintaining the simplicity of the matching approach
3Measurement precision
If additional terms are provided in the search query, then search specificity increases, but many relevant messages may be omitted due to brevity
Solution Approach 1:
The patent applies partial action by not requiring all query terms to be present in short messages. Instead, it uses a scoring system where messages receive partial credit for matching subsets of terms, allowing relevant messages to be retrieved even when they don't contain every query term, thus balancing specificity with recall
Solution Approach 2:
The patent changes the matching parameters by implementing a flexible scoring system that weights different term matches differently and allows for partial matches. This parameter adjustment enables the system to maintain search specificity while retrieving a sufficient quantity of relevant messages despite their brevity
Data Source
AI summary
Systems and methods of identifying and categorizing social network messages that are relevant to selected categories and text terms are provided. The frequency of text terms appearing in social network messages are calculated for multiple categories. Based on the calculated text term frequency, social network messages can be identified and/or categorized that match a provided set of text terms. Selecting and/or associating text terms and categories are determined by repeatedly analyzing social network messages.