The problem VibeCheck solves

Sentiment analysis, a specialized domain of natural language processing (NLP), focuses on discerning emotions and opinions from text, sorting them into categories like positive, negative, or neutral. This process quantifies subjective human expressions, driving applications such as brand monitoring and customer feedback analysis. It navigates complexities like context comprehension and cross-cultural linguistic variations, highlighting its intricate nature. Its ability to decode sentiment nuances and interpret subjective content sets it apart, making it both valuable and challenging in understanding human expression

How is SA unique and helpful in the productive sector?
Sentiment analysis, a specialized domain of natural language processing (NLP), focuses on discerning emotions and opinions from text, sorting them into categories like positive, negative, or neutral. This process quantifies subjective human expressions, driving applications such as brand monitoring and customer feedback analysis. It navigates complexities like context comprehension and cross-cultural linguistic variations, highlighting its intricate nature. Its ability to decode sentiment nuances and interpret subjective content sets it apart, making it both valuable and challenging in understanding human expression

How is it helpful in the Mental Health sector?
It collects user data from apps , stores it and analysis it, thus assisting in the following:
Monitoring Social Media and Forums
Assessing User Sentiment
Indentifying High-Risk Situations
Content Moderation
Feedback analysis
Public Health Campaigns
Early Intervention in cases of emerging mental trends or crisis
Research and Data Analysis

Challenges we ran into

Gathering huge amount of data to increase efficiency and accuracy of out Machine Learning model.
Training the ML model to give more accurate results