A Cross-Corpus Deep Learning Approach to Social Media Emotion Classification

Emotion Detection refers to the identification of emotions from contextual data in the form of written text, such as comments, posts, reviews, publications, articles, recommendations, conversations, and so on. Because of the Internet's exponential uptake and the recent coronavirus outbreak, social media platforms have become a crucial means of sharing thoughts and ideas throughout the entire globe, creating rapid data growth through users' contributions on various platforms. The necessity to acquire knowledge of their behaviors is a matter of great concern for both internet safety and privacy. In this study, we categorize emotional sentiments using deep learning models along with hybrid approaches such as LSTM, Bi-LSTM, and CNN+LSTM. When compared to existing state-of-the-art methods, the experiments showed that the suggested strategy is more robust and achieves an expressively higher quality of emotion detection with an accuracy rate of 94.16%, including strong F1-scores on complex and difficult emotion categories such as Fear (93.85%) and Anger (94.66%) through CNN+LSTM. © 2022 IEEE.

Mobile Application Based Teli-nutrition System for Covid-19 Pandemic

In Bangladesh, there is a shortage of legitimate nourishment data frameworks that can give fitting sustenance messages dependent on various rules for pregnant ladies and newborn children. Lack of healthy sustenance devastatingly affects people's wellbeing and prosperity and the monetary improvement of nations. Conversely, essential or tertiary health laborers couldn't offer vital assistance to them. With so many people becoming ill from the (COVID-19), poor weight control plans exacerbate pre-existing conditions, putting them at greater risk. Individuals living with chronic illnesses who have been diagnosed with COVID-19 must improve their mental health and count calories to ensure that they remain in good health. Look for direct and psychosocial support from suitably prepared wellbeing care experts, including community-based lay and peer guides. Venturing into nourishment counsel, advancing breastfeeding, and battling deception around COVID-19 transmission will offer assistance to protect the role of nutritious nourishment as a partner against sickness. Any health worker in Bangladesh can easily use this application. Our health laborers regularly neglect to convey legitimate nourishment data to moms. Such an instrument can be helpful in giving a proper method to show particular nourishment messages to mothers dependent on their wellbeing stages and dependent on their baby's age. The design of this application can provide a legitimate office for conveying sustenance messages to mothers and workers. This framework may have to be examined occasionally to meet the progression of client prerequisites and be applied properly. © 2021 IEEE.