Search Query Generation for Social Message Extraction
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Solution Overview
Problem
Existing methods for extracting relevant messages from social networking platforms rely on manual updates of keyword-based searches, leading to redundant and noisy conversations, making it difficult for businesses to track changing customer preferences and trending topics efficiently.
Innovation Solution
A method and system that utilize processors to identify influential customers and keywords by scoring their influence and connectivity, generating a search query that includes these elements to extract relevant messages while excluding spam customers and keywords.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If keyword-based search queries are manually updated, then relevant messages can be extracted, but the process requires continuous manual intervention and cannot automatically adapt to changing customer preferences and trending topics
Solution Approach 1:
The system enables self-service by automatically generating search queries using machine learning models that analyze customer behavior patterns, trending topics, and message relevance without requiring manual intervention. The model continuously adapts to changing preferences by learning from new data, making the system self-updating and self-optimizing
Solution Approach 2:
The system changes parameters by dynamically adjusting search query components based on learned patterns from customer behavior data. The machine learning model modifies keyword selections, weightings, and query structures automatically to adapt to evolving customer preferences and trending topics
2Measurement precision
If traditional search queries are used, then message extraction can be performed, but redundant and noisy conversations including spam messages are extracted making manual analysis difficult
Solution Approach 1:
The system applies local quality by assigning different weights and relevance scores to different parts of the search query and message set. The machine learning model identifies and emphasizes high-quality relevant messages while suppressing low-quality noisy content through localized adjustments in query construction and result ranking
Solution Approach 2:
The system discards harmful elements by automatically filtering out spam messages and redundant conversations through the learned model. The system recovers valuable information by prioritizing and extracting only the most relevant messages based on customer behavior patterns and trending topics
3Loss of information
If manual analysis of extracted messages is performed, then business organizations can identify customer preferences, but the process is time-consuming and inefficient
Solution Approach 1:
The system replaces the mechanical manual analysis process with an automated machine learning-based information extraction system. The model automatically analyzes extracted messages to identify customer preferences, trending topics, and key insights, substituting human analytical work with computational analysis that is both faster and scalable
Data Source
AI summary
A method and a system for generating a search query to extract one or more relevant messages from a plurality of messages shared over a computer network. The method includes extracting a plurality of keywords and information pertaining to a plurality of customers from the plurality of messages. Further, the method identifies a set of influential customers from the plurality of customers based on a first score and a second score. The method further includes extracting a set of influential keywords from a first set of messages based on a first number of occurrences of the plurality of keywords in the plurality of messages within a pre-defined time interval. The method further includes generating the search query that includes at least the set of influential customers and the set of influential keywords.