AI Customer Message Tagging for Sentiment Analysis
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Solution Overview
Problem
Companies face challenges in quickly organizing and analyzing vast amounts of unstructured customer data from social media and other sources to understand consumer demand and sentiment effectively.
Innovation Solution
A system and method utilizing artificial intelligence models, including machine learning techniques such as deep belief networks and convolutional neural networks, to automatically tag and analyze customer messages, providing industry-specific and sentiment-based tags, and allowing for manual adjustments, which are then displayed on a dashboard for visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If companies manually organize and analyze customer data from social media and other sources, then they can obtain useful information about consumer demand, but the process is time-consuming and cannot keep up with the speed at which data is created
Solution Approach 1:
The patent replaces manual mechanical analysis of customer messages with automated artificial intelligence models. The AI system processes unstructured text data from social media and other sources, automatically extracting insights about consumer demand and sentiment without human intervention, thereby resolving the contradiction between analysis accuracy and time consumption.
2Loss of information
If companies invest millions in market research to understand consumer needs, then they can obtain valuable insights, but the cost is prohibitively high
Solution Approach 1:
The patent enables companies to conduct their own market research using automated AI models that analyze customer messages independently. Instead of relying on expensive external market research firms, the system allows companies to self-serve by processing their own data through the AI platform, significantly reducing research costs while maintaining insight completeness.
3Stability of the object's composition
If companies use traditional data organization methods, then they can structure the data, but the data remains difficult to use for quick decision-making
Solution Approach 1:
The patent transforms unstructured text data into structured, actionable insights by changing the parameters of data representation. The AI models convert raw customer messages into categorized tags, sentiment scores, and key themes, making the data both organized and immediately usable for quick decision-making processes.
4Loss of information
If companies analyze all customer messages manually, then they can capture all insights, but the complexity and resource requirements become unmanageable
Solution Approach 1:
The patent segments the complex task of analyzing all customer messages into multiple independent AI models that process different aspects separately. Each model focuses on specific features such as sentiment detection, topic classification, or entity recognition, making the overall system more manageable while maintaining comprehensive analysis coverage.
Data Source
AI summary
A system and method for automatically tagging customer messages using artificial intelligence models. A server gateway processes the customer messages via an artificial intelligence system featuring artificial intelligence models. The artificial intelligence system analyzes the customer messages to determine the content by tagging words and phrases with industry specific tags (e.g. product feedback, product defects, shipping delays, etc) as well as tags based on sentiment type (e.g., negative, positive, neutral, sarcasm, mixed) and contact type (e.g., delivery person, influencer, postsale, presale). The artificial intelligence system returns the tagged results, which are transmitted by the server gateway to a user computational device or another system for visualization.