Stress detection using a novel python based sentiment analysis technique

To improve the accuracy of stress identification, the research integrates multimodal sources, such as auditory and visual cues, in addition to textual data. To identify non-verbal signs of stress, speech patterns, facial expressions, and physiological signals are examined. This allows for a more thorough comprehension of a person’s mental health. The impact of social networks on stress dynamics is also taken into account in the suggested paradigm. Through the modeling of opinion diffusion throughout social circles, the research seeks to find patterns of collective stress and how these affect people’s well-being. Techniques for social network analysis are used to find hidden relationships and exchanges that help spread negative emotions linked to stress. Major draw back associated with all these methods is that they are binary in nature and mostly suitable for binary classification problems only, so individuals in the society are supposed to have stress or not is determined but amountofstressisnotconsidered. In real life an individuals may have less stress, more stress or no stress. Moreover Sentiment analysis is challenging and not usually reliable due to sarcasm, regional or culturally specific jargon, and language usage. Although humans are able to recognize sarcasm with ease, artificial neural networks still struggle to identify it and provide a mood score that is accurate. Additionally, unless localized training datasets are utilized for sentiment classification, the local vernacular spoken by people from different cultures or areas would typically have weak sentiment scores. In this work a python based sentiment analysis technique is employed to perfectly analyze different kinds of social media messages.