Emotion Analysis Feedback for Interactive Diary Visualization
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Solution Overview
Problem
Traditional diary-writing methods fail to sustain user interest and may exacerbate emotional distress due to monotony and the complexity of incorporating images, lacking effective emotional support for addressing issues like anxiety and depression.
Innovation Solution
An apparatus and method utilizing a recorder, negative-text analysis module, positive-text adding module, and visualization module to analyze diary entries, generate positive feedback responses, and create visual images based on a PERMA framework, enhancing emotional support through personalized prompts and images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional diary-writing is used for emotional release, then emotional expression is achieved, but user interest wanes over time due to monotony
Solution Approach 1:
The system analyzes diary entries using NLP to detect emotions and provides automated feedback responses with positive orientations and actionable suggestions. This feedback loop transforms单调日记 writing into an interactive emotional support experience, maintaining user engagement while preserving emotional expression effectiveness
Solution Approach 2:
The system dynamically changes the parameters of diary-writing by introducing emotion detection accuracy, feedback response variety, and visualization options. These parameter changes transform the static diary format into a dynamic, adaptive experience that maintains user interest while preserving therapeutic value
2Reliability
If images and illustrations are incorporated into diaries to enhance expression, then emotional expression is improved, but user experience becomes complicated and consistent journaling is discouraged
Solution Approach 1:
The system introduces AI emotion detection and automated feedback as intermediaries between the user and the diary content. This intermediary layer provides emotional support and guidance without requiring users to manually incorporate complex visual elements, thus improving emotional expression while maintaining simplicity
Solution Approach 2:
The system enables self-service by automatically analyzing emotions and generating feedback responses without requiring user intervention for complex operations. Users simply write their entries while the system handles emotion detection, analysis, and feedback generation, maintaining simplicity while enhancing emotional expression
3Reliability
If negative emotions are expressed in writing, then emotional release is achieved, but feelings of anxiety and depression may be intensified
Solution Approach 1:
The system converts potentially harmful negative emotion expression into beneficial outcomes by detecting negative emotions through NLP analysis and automatically generating positive feedback responses. This transforms the harmful effect of intensifying negative feelings into a beneficial therapeutic intervention that maintains emotional release while reducing anxiety and depression intensity
Solution Approach 2:
The system applies preliminary anti-action by proactively detecting negative emotions before they can intensify and automatically providing positive feedback to counteract them. This preemptive approach prevents the escalation of anxiety and depression while preserving the cathartic value of emotional expression
Data Source
AI summary
An apparatus for analyzing emotions of diaries and generating feedback response on diaries is provided. The apparatus includes a recorder, a negative-text analysis module, a positive-text adding module, and a visualization module. The recorder is configured to receive and store a text article. The negative-text analysis module is configured to analyze the text article and determine a negativity level and generate a basis for judgment according to an analyzing result with respect to the text article. The positive-text adding module is configured to receive the basis from the negative-text analysis module, generate feedback response that is positively oriented relative to the text article, and combine the text article with the feedback response to form a content. The visualization module is configured to visualize the content for creating a visual image.