NLP Sentiment Analysis for Network Traffic Reduction
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Solution Overview
Problem
Communications networks are overwhelmed with irrelevant communications, straining their performance and increasing latency, as many messages are unwanted by users, particularly during stressful times, which can negatively impact user sentiment towards services.
Innovation Solution
Implementing natural language processing (NLP) to detect sentiment triggers in user communications, assigning a communication suppression flag to user accounts, preventing unwanted communications from being sent, thereby reducing network traffic and improving user sentiment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If communications are sent to all users without filtering, then user information delivery is complete, but network traffic increases and performance deteriorates
Solution Approach 1:
The system performs preliminary analysis of user communications to detect sentiment triggers before assigning suppression flags. This advance preparation allows the system to proactively identify users who should not receive communications, preventing unnecessary traffic before it occurs rather than filtering after transmission.
Solution Approach 2:
The system extracts and removes unwanted communications from the network traffic stream by identifying users with suppression flags and blocking their messages. This extraction process separates relevant communications from irrelevant ones, reducing overall network traffic while maintaining performance for valid messages.
2Quantity of substance
If communications suppression flags are assigned based on sentiment analysis, then unwanted communications are reduced, but processing complexity increases
Solution Approach 1:
The system enables users to essentially manage their own communication preferences through their natural interaction patterns. By analyzing user communications and automatically assigning suppression flags based on detected sentiment triggers, the system allows users to control their own message reception without manual intervention, reducing the operational complexity burden.
Solution Approach 2:
The system continuously monitors user communications and uses sentiment analysis feedback to dynamically adjust suppression flag assignments. This feedback loop allows the system to learn from user interactions and automatically refine its filtering decisions, reducing the need for complex manual configuration and improving accuracy over time.
3Loss of information
If all communications are delivered to ensure complete information, then user information needs are met, but latency increases due to network load
Solution Approach 1:
The system performs preliminary identification of suppression candidates through sentiment analysis before communications are transmitted. By pre-determining which users should not receive messages based on their recent communication patterns and detected sentiment triggers, the system avoids unnecessary transmissions that would contribute to latency, while ensuring complete information delivery to appropriate users.
Data Source
AI summary
Some embodiments of the present application include obtaining communications with a user associated with a user account of a service. The communications may be analyzed using natural language processing to detect an instance of a first trigger within the communications. If detected, a data flag can be assigned to a data structure associated with the user account. In some embodiments, subsequent to the data flag being assigned to the data structure, a request may be obtained for a communication to be sent to the user. For instance, the request may be obtained responsive to determining that a value associated with the user account satisfies a condition. Based on the data flag and the request, the communication may be prevented from being sent to the user.