Sentiment Indicator in Graphical User Interface for Text Analysis
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Solution Overview
Problem
In distributed computing environments, written communication often fails to convey intentions precisely, leading to misunderstandings and frustration, especially when negative sentiments are prevalent, as they can escalate quickly in collaborative platforms.
Innovation Solution
A system that estimates the sentiment of text in real-time within a graphical user interface (GUI) using a collaboration server and client devices, displaying sentiment indicators and providing suggestions to improve the sentiment, thereby preventing the publication of excessively negative comments and reducing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time sentiment analysis is implemented in the GUI, then sentiment awareness is improved, but device complexity increases
Solution Approach 1:
A sentiment analysis service acts as an intermediary between the collaboration server and client devices. The service receives text data, performs sentiment analysis, and returns sentiment indicators to the GUI, which then displays them to users. This mediator approach allows complex analysis functionality to be added without directly complicating the core collaboration system architecture.
Solution Approach 2:
The patent replaces manual sentiment assessment with automated computational sentiment analysis. Instead of users manually evaluating the sentiment of text communications, the system uses natural language processing algorithms to automatically analyze and determine sentiment, substituting mechanical human judgment with computational analysis.
2Reliability
If sentiment analysis and suggestions are provided in real-time, then communication quality is improved, but resource utilization increases
Solution Approach 1:
The system performs sentiment analysis selectively rather than continuously on all text. It triggers analysis based on specific conditions such as when text is submitted or when certain keywords are detected, performing partial analysis only when necessary rather than exhaustive continuous analysis, thus reducing overall resource consumption while maintaining communication quality.
3Adaptability or versatility
If negative sentiments are allowed to spread freely, then communication freedom is maintained, but harmful factors increase
Solution Approach 1:
The system implements feedback by providing sentiment indicators to users in real-time as they compose or review text. When negative sentiment is detected, the GUI displays warnings or suggestions to the user, allowing them to adjust their communication before submission. This feedback loop enables users to maintain communication freedom while being aware of and able to control the sentiment impact of their messages.
Solution Approach 2:
The system applies preliminary anti-action by detecting negative sentiment before text is published and providing users with opportunities to revise or cancel the communication. Rather than allowing negative sentiments to spread freely, the system proactively intervenes by warning users and suggesting alternatives before the harmful communication is disseminated to other users.
Data Source
AI summary
A method of presenting a sentiment indicator with entered text is disclosed. The method comprises causing a presentation of a graphical user interface (GUI) comprising a screen, the screen including a first area for receiving text and a second area next to the first area for showing a sentiment indicator; receiving text via the first area; before receiving a request for submitting the text to a server for access by other hardware processors in a distributed computing environment: determining whether a triggering condition for estimating a sentiment is satisfied; in response to determining that the triggering condition is satisfied, estimating a sentiment of the text; and causing displaying the sentiment indicator in the second area with a sentiment indicator value corresponding to the estimated sentiment of the text.