In the rapidly evolving domain of e-commerce, analyzing customer feedback through reviews is crucial, particularly for understanding and enhancing consumer experience in the Bangladeshi market. Our comprehensive dataset, derived from two Bangladeshi e-commerce platforms, Daraz and Pickaboo, features a diverse collection of reviews in both Bengali and English, covering a broad range of products. These reviews are not only rich in linguistic variety but also encapsulate a spectrum of emotions, some even conveyed through emojis, offering a deep dive into consumer sentiment. Expert annotators have meticulously examined and categorized each review, classifying emotions into five distinct types - Happiness, Sadness, Fear, Anger, and Love - and sentiments into Positive (Happiness, Love) and Negative (Sadness, Anger, Fear) categories. This level of detailed annotation enables precise assessments of customer emotions and preferences, which are essential for evaluating and improving existing product offerings. Moreover, the insights gleaned from this dataset are invaluable for guiding future product development and uncovering new opportunities in the dynamic Bangladeshi market. Ultimately, this dataset not only serves as a significant resource for sentiment analysis using natural language processing (NLP) techniques but also contributes valuable insights into the unique consumer behavior patterns in Bangladesh, enriching the NLP community's understanding of diverse market dynamics.

The agricultural sector is a vital component of Bangladesh's economy, but its agri-food supply chain faces signifi-cant inefficiencies primarily due to the involvement of numerous intermediaries. This complexity not only reduces the profits for farmers but also affects the overall transparency and efficiency of the supply chain. This study aims to em-ploy blockchain technology to transform the traditional agri-food supply chain in Bangladesh, focusing on increasing transparency, enhancing efficiency, and improving profitability for farmers, thus potentially bolstering the entire agri-food ecosystem in the country. The research involves setting up a blockchain-based smart contract on the Ethereum Blockchain network. This approach guarantees that all transactions within the agri-food supply chain are transparent, traceable, and accountable. Additionally, the study develops a web application to facilitate user interaction with the smart contract, enhancing accessibility and usability. Performance analysis and testing of the implemented smart con-tract demonstrate its capability to handle a significant volume of transactions without compromising on performance. The solution effectively reduces the dependency on intermediaries, thereby increasing the profit margins for the farm-ers involved. The integration of blockchain technology in the agri-food supply chain has shown promising results in enhancing transparency and efficiency. It lays a solid foundation for future improvements and suggests a scalable model that could be applied to other sectors within the country to further enhance agricultural practices and economic growth.

Papaya, renowned for its nutritional benefits, represents a highly profitable crop. However, it is susceptible to various diseases that can significantly impede fruit productivity and quality. Among these, leaf diseases pose a substantial threat, severely impacting the growth of papaya plants. Consequently, papaya farmers frequently encounter numerous challenges and financial setbacks. To facilitate the easy and efficient identification of papaya leaf diseases, a comprehensive dataset has been assembled. This dataset, comprising approximately 1400 images of diseased, infected, and healthy leaves, aims to enhance the understanding of how these ailments affect papaya plants. The images, meticulously collected from diverse regions and under varying weather conditions, offer detailed insights into the disease patterns specific to papaya leaves. Stringent measures have been taken to ensure the dataset's quality and enhance its utility. The images, captured from multiple angles and boasting high resolution are designed to aid in the development of a highly accurate model. Additionally, RGB mode has been employed to meticulously capture each detail, ensuring a flawless representation of the leaves. The dataset meticulously identifies and categorizes five primary types of leaf diseases: Leaf Curl (inclusive of its initial stage), Papaya Mosaic, Ring Spot, Mites (specifically, those affected by Red Spider Mites), and Mealybug. These diseases are recognized for their detrimental effects on both the leaves and the overall fruit production of the papaya plant. By leveraging this curated dataset, it is possible to train a model for the real-time detection of leaf diseases, significantly aiding in the timely identification of such conditions.