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p-Index From 2020 - 2025
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All Journal International Journal of Advanced Science Computing and Engineering HISTORICAL: Journal of History and Social Sciences The Eastasouth Journal of Information System and Computer Science
Rahman, Md Habibur
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Social Sciences

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Predicting Customer Sentiment in Social Media Interactions: Analyzing Amazon Help Twitter Conversations Using Machine Learning Arif, Md; Hasan, Mehedi; Al Shiam, Sarder Abdulla; Ahmed, Md Parvez; Tusher, Mazharul Islam; Hossan, Md Zikar; Uddin, Aftab; Devi, Suniti; Rahman, Md Habibur; Ali Biswas, Md Zinnat; Imam, Touhid
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 2 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.2.211

Abstract

Social media platforms, particularly Twitter, have become essential sources of data for various applications, including marketing and customer service. This study focuses on analyzing customer interactions with Amazon's official support account, "@AmazonHelp," to understand and predict changes in customer sentiment during these interactions. Using the Twitter API, we extracted English-language tweets mentioning "@AmazonHelp," pre-processed the data, and categorized conversations to facilitate analysis. The primary objectives were to classify changes in customer sentiment and predict the overall sentiment change based on initial sentiment. We conducted experiments using multiple machines learning algorithms, including K-nearest neighbor, Naive Bayes, Artificial Neural Network, Bayes Net, Support Vector Machine, Logistic Regression, and Bagging with RepTree. Our dataset comprised over 6,500 conversations, filtered to include those with four or more tweets. Results indicated that K-nearest neighbor and Support Vector Machine offered the best balance between accuracy and F-measure, while Bagging with RepTree achieved the highest accuracy but had a lower F-measure. This study demonstrates the potential of integrating sentiment analysis and machine learning to effectively predict customer sentiment in social networks, providing valuable insights for improving customer engagement strategies.
Indigenous Water Symbolism and Management: A Comparative Study on Ecologies of Rain and Intellectual Appropriation in Bangladesh, India, the US, and Germany Rahman, Md Habibur; Md. Mobashir Rahman; Imroze Asif Khan
HISTORICAL: Journal of History and Social Sciences Vol. 4 No. 3 (2025): History and Cultural Innovation
Publisher : Perkumpulan Dosen Fakultas Agama Islam Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58355/historical.v4i3.202

Abstract

Indigenous and folk water representations are examined using the conceptual lenses "Rainline" and "Waterline," which investigate how rain interacts symbolically and pragmatically in environmental traditions. The study contrasts eco-critical research from Bangladesh, India, the US, and Germany to examine how songs, rituals, myths, and proverbs reflect cultural reactions to rain and water as sacred and ecological requirements. The studies center on Bangladesh's Garo, Santal, and Rajbongshi people, Indian tribal and Vedic rain customs, Native American rain invasions, and European farming. Various societies both cognitively modify and symbolically control natural forces. In emotional and agricultural life, rainfall is required, negotiated, and acknowledged; these ideas help to organize these symbolic surroundings. The article combines folklore research, thematic coding, and comparative cultural hermeneutics. These approaches are not relics; they assert that they are dynamic ecological knowledge systems with sustainable knowledge. It suggests considering localized, culturally informed responses to water and temperature as means of climate adaptation.
Explainable AI Framework for Precision Public Health in Metabolic Disorders: A Federated, Multi-Modal Predictive Modelling Approach for Early Detection and Intervention of Type 2 Diabetes Rahman, Md Habibur; Khan, Md Nazibullah; Das, Sachin; Uddin, Borhan
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 02 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i02.759

Abstract

One of the biggest public health problems of the twenty-first century is metabolic disorders, especially Type 2 diabetes (T2D). Morbidity, mortality, and medical expenses can be significantly decreased by early detection of at-risk people. However, nonlinear, multi-factorial, and high-dimensional interactions that influence the development of disease are not well captured by traditional risk-scoring methods. In order to predict and interpret the risk of type 2 diabetes and related metabolic disorders, this study creates an Explainable AI (XAI) framework for precision public health that combines multi-modal data, such as genomic profiles, lifestyle factors, socioeconomic determinants, and electronic health records (EHR). We create a federated, hybrid model that combines Random Forest classifiers, Deep Neural Networks (DNN), and Gradient Boosting Machines (LightGBM/XGBoost), building on federated and ensemble learning paradigms. Shapley Additive Explanations (SHAP) and counterfactual analysis are used to uncover personalized, actionable risk profiles in order to attain explainability. Harmonized multi-institutional datasets with over 200,000 records gathered from several U.S. health systems are used to train the model. The results show a calibrated Brier score of 0.12, sensitivity of 89%, specificity of 87%, and AUC of 0.93 ± 0.01. The socioeconomic deprivation index, polygenic risk score, BMI slope, and HbA1c trajectory are the main factors, according to SHAP study. Federated deployment protects data privacy while preserving performance. These results show that federated, explainable AI pipelines can facilitate population-based, privacy-preserving, andThe goal of precision public health is being advanced by large-scale early-warning systems for managing metabolic diseases.
Co-Authors Ahmed, Md Parvez Al Shiam, Sarder Abdulla Ali Biswas, Md Zinnat Arif, Md Das, Sachin Devi, Suniti Hasan, Mehedi Hossan, Md Zikar Imam, Touhid Imroze Asif Khan Khan, Md Nazibullah Md. Mobashir Rahman Tusher, Mazharul Islam Uddin, Aftab Uddin, Borhan