Automated Response System Using Social Media Question Extraction
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Solution Overview
Problem
Current automated systems for retrieving data often fail to provide accurate and relevant answers due to insufficient data, incorrect categorization, or an overabundance of data without proper context, leading to unreliable responses to user queries.
Innovation Solution
An automated response system that extracts question and answer pairs from social media posts and associated comments, using classification models and algorithms to identify questions, determine relevant answers based on context and user characteristics, and store them in a knowledge base for future reference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems use default web search operations for unseen questions, then they can provide some response, but the accuracy and relevance of answers deteriorates
Solution Approach 1:
The system performs preliminary categorization of available data and pre-identifies context information before a question is asked. By organizing data into categories and pre-processing context information, the system is better prepared to handle unseen questions with higher accuracy rather than relying on default web search operations.
Solution Approach 2:
The system introduces an intermediary categorization layer between the question and the data retrieval process. This intermediary structure helps bridge the gap between unseen questions and relevant answers by routing queries through organized data categories, improving both accuracy and relevance of responses.
2Adaptability or versatility
If the system stores and processes more data to cover more questions, then the coverage improves, but the difficulty of properly categorizing and identifying context increases
Solution Approach 1:
The system segments the large volume of data into distinct, organized categories. By dividing the data repository into manageable segments with clear classifications, the system can handle diverse questions more effectively without being overwhelmed by the sheer volume of information, thus reducing categorization complexity while maintaining versatility.
Solution Approach 2:
The system adds contextual dimensions to the data organization structure. By incorporating context information as an additional dimension alongside traditional categorization, the system can better handle diverse questions without linearly increasing complexity, as the contextual layer provides efficient routing guidance.
3Quantity of substance
If the system provides multiple known answers to a question, then the completeness of information improves, but the ability to identify the most relevant context-specific answer deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms that evaluate the relevance of multiple candidate answers against the specific context of the question. By assessing context matching accuracy and using this feedback to rank or select the most appropriate answer, the system maintains answer completeness while improving context-specific relevance through iterative refinement.
Data Source
AI summary
A system for answering user questions can provide answers from a knowledge base that stores question/answer pairs. These pairs can be associated with characteristics of the asking user so that, when subsequent users ask similar questions, answers can be selected that have been identified as most relevant to that type of user. The question/answer pairs in the knowledge base can be identified from social media posts where the original post contains a question and one or more comments on the post provide an answer. Posts can be identified as containing a question using a question classification model. A post comment can be identified as an answer based on: whether the question poster responded positively to the comment, whether the comment has similar keywords to the question, whether the comment has the characteristics of an answer, and how often a similar answer has been provided for similar questions.