Social media platforms like X (formerly Twitter) generate vast volumes of user-generated content that provide real-time insights into public sentiment. Despite the widespread use of traditional machine learning methods, their limitations in capturing contextual nuances in noisy social media text remain a challenge. This study leverages the Sentiment140 dataset, comprising 1.6 million labeled tweets, and develops predictive models for binary sentiment classification using Naive Bayes, Logistic Regression, and the transformer-based BERT model. Experiments were conducted on a balanced subset of 12,000 tweets after comprehensive NLP preprocessing. Evaluation using accuracy, F1-score, and confusion matrices revealed that BERT significantly outperforms traditional models, achieving an accuracy of 89.5% and an F1-score of 0.89 by effectively modeling contextual and semantic nuances. In contrast, Naive Bayes and Logistic Regression demonstrated reasonable but consistently lower performance. To support practical deployment, we introduce SentiFeel, an interactive tool enabling real-time sentiment analysis. While resource constraints limited the dataset size and training epochs, future work will explore full corpus utilization and the inclusion of neutral sentiment classes. These findings underscore the potential of transformer models for enhanced public opinion monitoring, marketing analytics, and policy forecasting.

Background: Food security and dietary diversity are essential determinants of maternal health, particularly among pregnant women in refugee populations who face heightened vulnerabilities due to displacement and inadequate living conditions. This study examines the impact of food security on dietary diversity and nutritional intake among pregnant Rohingya women residing in the makeshift camps of Ukhiya, Cox’s Bazar. Methods: A descriptive cross-sectional study was conducted among 96 pregnant Rohingya women from June to September 2022. Data were collected using structured questionnaires assessing socio-demographic characteristics, food security, and dietary diversity. Food security was evaluated using the Household Food Insecurity Access Scale (HFIAS), while dietary diversity was assessed through a 24-hour dietary recall and a 7-day food frequency questionnaire. Data were analyzed using SPSS (Version 26) and Stata (Version 13), employing descriptive statistics and chi-square tests to examine associations. Results: Most participants (57.3%) were food secure, and 85.4% demonstrated high dietary diversity, consuming seven or more food groups. However, 21.9% of households experienced severe food insecurity, highlighting ongoing challenges in food access. The highest consumption was observed for starch, flesh foods, dark green leafy vegetables, and vitamin A-rich fruits and vegetables (99.0%), while dairy products (69.8%) and organ meat (34.4%) were consumed less frequently. Despite high dietary diversity, severe food insecurity persists, indicating gaps in food assistance programs. Conclusions: While food support programs appear to contribute to high dietary diversity among pregnant Rohingya women, severe food insecurity remains a significant concern. Strengthening food security interventions, improving access to diverse nutrient-rich foods, and integrating sustainable food assistance models are essential to addressing these challenges. Future research should explore long-term strategies to enhance food security and assess the impact of targeted nutritional interventions on maternal health outcomes in refugee settings.

Background: Proper umbilical cord care prevents neonatal infections and reduces neonatal mortality. Despite global recommendations for evidence-based cord care practices, traditional beliefs, and inadequate maternal knowledge often lead to unsafe practices, particularly in low-resource settings like Bangladesh. This study aimed to assess the understanding of umbilical cord care among mothers of neonates in Sherpur District, Bangladesh, and identify factors associated with knowledge levels. Methods: A descriptive cross-sectional study was conducted from July to October 2020 at Sherpur Sadar Hospital. A total of 193 mothers of neonates were recruited using a non-randomized purposive sampling method. Data was collected through a pre-tested, semi-structured, interviewer-administered questionnaire. Knowledge levels were categorized as "Good" (>6) or "Poor" (≤6) based on responses to 10 structured questions. Statistical analyses, including chi-square tests and crude odds ratios (COR), were performed to identify socio-demographic factors associated with knowledge levels. Results: Of the 193 participants, 48.7% demonstrated "Good" knowledge, while 51.3% had "Poor" knowledge. Education level (p = 0.01), occupation (p = 0.02), family type (p < 0.001), and family size (p = 0.04) were significantly associated with knowledge levels. Mothers with higher education and those from joint families exhibited better knowledge. However, 28.5% of respondents were unaware of the typical umbilical cord-shedding timeframe, and 44% could not identify signs of infection. Unsafe practices, such as using medications (14.5%) or hot compression (7.2%) for drying the cord, were reported. Conclusion: The study reveals significant gaps in maternal knowledge regarding umbilical cord care in Sherpur District, driven by socio-demographic disparities and cultural practices. Targeted health education programs, emphasizing evidence-based cord care practices and leveraging local social structures, are urgently needed to improve neonatal health outcomes in similar resource-limited settings. Future research should evaluate the effectiveness of these interventions to inform policy and practice.

Background: Nursing students are exposed to significant stress due to academic and clinical demands, which can adversely affect their mental health, academic performance, and future clinical competence. Despite the global acknowledgment of this issue, limited research has been conducted to explore the prevalence and determinants of stress among nursing students in Bangladesh. Methods: This cross-sectional study was conducted from December 2023 to February 2024 among 372 nursing students enrolled in selected nursing colleges in Bangladesh. A purposive sampling technique was used, and data was collected using a semi-structured questionnaire. The questionnaire assessed socio-demographic characteristics, academic challenges, and psychological symptoms, with mental health stress measured using a Likert scale. Descriptive statistics and Chi-square tests were used to analyze the data, with a 95% confidence interval applied to all analyses. Results: The findings revealed that 31.7% of nursing students experienced severe stress, 23.9% reported moderate stress, and 16.7% had mild stress. Age, academic semester, and course load difficulties were significantly associated with stress levels (p < 0.05). Psychological symptoms such as anxiety, difficulty concentrating, and loss of interest in activities were also significantly linked to higher stress levels. Notably, students in their first semester and those reporting harder course loads were more likely to experience stress. However, gender was not significantly associated with stress levels. Conclusions: This study underscores the high prevalence of stress among nursing students in Bangladesh, driven by academic and clinical challenges and psychological symptoms. The findings highlight the need for targeted interventions, such as stress management training, enhanced mental health support, and policies to alleviate academic pressures. Future research should explore longitudinal trends in stress and evaluate the effectiveness of interventions to support a resilient nursing workforce.