A comprehensive dataset for sentiment and emotion classification from Bangladesh e-commerce reviews

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.

ArsenicSkinImageBD: A comprehensive image dataset to classify affected and healthy skin of arsenic-affected people

Compared to other popular research domains, dermatology got less attention among machine learning researchers. One of the main concerns for this problem is an inadequate dataset since collecting samples from the human body is very sensitive. In recent years, arsenic has emerged as a significant issue for dermatologists. Arsenic is a highly toxic substance found in the earth's crust whose small amounts can be very injurious to the human body. People who are exposed to arsenic for a long time through water and food can get cancer and skin lesions. With a view to contributing to this aspect, this dataset has been organized with the help of which the researchers can understand the impact of this contamination and design a solution using artificial intelligence. To the best of our knowledge, this is the first standard, easy-to-use, and open dataset of arsenic diseases. The images were collected from four places in Bangladesh, under the Department of Public Health Engineering, Chapainawabganj, where they are working on arsenic contamination. The dataset has 8892 skin images, with half of them showing people with arsenic effects and the other half showing mixed skin images that are not affected by arsenic. This makes the dataset useful for treating people with arsenic-related conditions. Eventually, this dataset can attract the attention of not only the machine learning researchers, but also scientists, doctors, and other professionals in the associated research field.

Comprehensive dataset of annotated rice panicle image from Bangladesh

Bangladesh's economy is primarily driven by the agriculture sector. Rice is one of the staple food of Bangladesh. The count of panicles per unit area serves as a widely used indicator for estimating rice yield, facilitating breeding efforts, and conducting phenotypic analysis. By calculating the number of panicles within a given area, researchers and farmers can assess crop density, plant health, and prospective production. The conventional method of estimating rice yields in Bangladesh is time-consuming, inaccurate, and inefficient. To address the challenge of detecting rice panicles, this article provides a comprehensive dataset of annotated rice panicle images from Bangladesh. Data collection was done by a drone equipped with a 4 K resolution camera, and it took place on April 25, 2023, in Bonkhoria Gazipur, Bangladesh. During the day, the drone captured the rice field from various heights and perspectives. After employing various image processing techniques for curation and annotation, the dataset was generated using images extracted from drone video clips, which were then annotated with information regarding rice panicles. The dataset is the largest publicly accessible collection of rice panicle images from Bangladesh, consisting of 2193 original images and 5701 augmented images.