Multi-Platform Communication Network Sentiment Analysis
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Solution Overview
Problem
Communication service providers face challenges in gauging customer experience due to the high volume of customer comments across multiple platforms, making it difficult to effectively analyze sentiment and identify actionable insights.
Innovation Solution
An automated sentiment analysis system utilizing a pre-processing module, NLP neural network, and post-processing module to collect, process, and classify customer comments from various platforms, generating actionable insights and reports for improving customer experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of customer comments is performed, then analysis accuracy can be maintained, but the time and resources required increase significantly due to high comment volume
Solution Approach 1:
The patent replaces manual mechanical analysis of customer comments with an automated natural language processing (NLP) system. The NLP model automatically processes and classifies customer feedback, eliminating the need for human analysts to manually review each comment while maintaining consistent sentiment analysis accuracy across large volumes of data.
Solution Approach 2:
The patent creates a computational model that replicates human sentiment analysis capabilities. By training the NLP system on labeled customer feedback data, the model learns to copy and reproduce human-like sentiment classification, enabling automated processing that matches manual analysis quality without the time constraints.
2Productivity
If automated processing of customer comments is implemented, then processing speed increases, but analysis precision may deteriorate due to lack of human judgment
Solution Approach 1:
The patent performs preliminary training of the NLP model using a dataset of customer comments that have been manually labeled with sentiment classifications. This preliminary action of training the model on high-quality labeled data enables the automated system to achieve high precision from the outset, rather than requiring iterative manual correction during deployment.
Solution Approach 2:
The system incorporates feedback mechanisms where the NLP model's classifications can be reviewed and corrected, with corrected data fed back into the training process to continuously improve accuracy. This feedback loop ensures that the automated system maintains and enhances precision over time while preserving high processing throughput.
3Loss of information
If comprehensive customer feedback from multiple platforms is collected, then data completeness improves, but system complexity increases due to integration requirements
Solution Approach 1:
The patent implements a universal data collection framework that can interface with multiple different platforms (social media, review sites, customer service systems) through standardized methods. This multi-functional approach allows the system to collect comprehensive feedback from diverse sources without requiring separate complex integration solutions for each platform.
Solution Approach 2:
The system employs intermediary components such as APIs and data normalization layers that mediate between various customer feedback platforms and the core NLP analysis system. These intermediaries handle platform-specific protocols and formats, translating them into a unified structure that simplifies downstream processing while maintaining complete data collection.
4Loss of information
If detailed sentiment analysis is performed on each comment, then insight quality improves, but computational resources required increase
Solution Approach 1:
The patent segments the customer feedback analysis process into multiple levels: initial sentiment classification, issue category identification, and detailed sentiment analysis. This segmentation allows the system to apply computationally intensive detailed analysis only to comments that require it, while processing the majority of feedback with lighter, faster classification methods, thereby reducing overall computational resource consumption.
Solution Approach 2:
The system applies different levels of analysis quality to different types of comments based on their characteristics. High-priority comments, those with ambiguous sentiment, or comments from valuable customer segments receive detailed analysis, while routine feedback receives standard classification. This local quality approach optimizes computational resource allocation while maintaining insight quality where it matters most.
Data Source
AI summary
A method for sentiment analysis regarding a communication network of a communication service provider (CSP) includes collecting a set of customer comments from one or more online platforms. Each customer comment includes text and is related to the communication network of the CSP. The method further includes generating a profile for each customer comment in the set of customer comments, providing the text of each customer comment to a natural language processing (NLP) neural network, using the NLP neural network to generate at least a sentiment classification and an issue classification for each customer comment based on the text of the customer comment, generating one or more reports based on one or more of the sentiment classification and the issue classification of each customer comment, and transmitting at least one report to a department of the CSP based on one or more of the sentiment classification or the issue classification.