Seasonal Affective Disorder (SAD) Dataset - 4646
- Citation Author(s):
-
Tazkia Tasnim Bahar Audry (Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh)Md. Jahangir Alam Alam (Department of Electrical and Computer Engineering, Morgan State University, 1700, E Cold Spring Ln, Baltimore, MD 21251, United States)Zaheed Ahmed Bhuiyan
(Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh)
Md. Motaharul Islam
(Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh)
Mohammad Mehedi Hassan
(Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia)
- Submitted by:
- Zaheed Ahmed Bhuiyan
- Last updated:
- DOI:
- 10.21227/ztka-qy28
- Data Format:
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Abstract
We have obtained data from May 2022 to October 2023 for our suggested framework modelling. This set of data incorporates seasonality-related speech, which we convert into text, Facebook, and Twitter posts. On the whole, 4646 data elements have been acquired, comprising 3716 representing affected individuals and the remainder of 930 representing unaffected individuals, which generated a proportional 4:1 ratio. To further enhance the effectiveness of the system, we were able to employ the Synthetic Minority Over-sampling Technique (SMOTE) for balancing the dataset using oversampling, resulting in a balance between affected and unaffected classes. After we balance our data, we proceed to implement our proposed machine learning algorithms.
Instructions:
1. Accessing the Dataset: Download the dataset file from the IEEE DataPort repository.
2. Dataset Usage:
- Use the provided columns to explore relationships between social media activity, speech-related text, and the likelihood of Seasonal Affective Disorder.
- Apply machine learning algorithms for predictive modeling using features such as age, gender, social media metrics, and text content.
3. Data Preprocessing:
- The dataset has been preprocessed to balance classes using Synthetic Minority Over-sampling Technique (SMOTE).
- Explore the provided data dictionaries for a better understanding of each feature.
4. Citation:
If you use this dataset in your research or publication, kindly cite it as follows:
Tazkia Tasnim Bahar Audry, Md. Jahangir Alam, Zaheed Ahmed Bhuiyan, Md. Motaharul Islam and Mohammad Mehedi Hassan “Seasonal Affective Disorder (SAD) Dataset - 4646,” IEEE DataPort, 2023, [Online]. Available: https://dx.doi.org/10.21227/ztka-qy28.
5. Contact Information:
For any questions or clarifications, please contact TAZKIA TASNIM BAHAR AUDRY at taudry191189@bscse.uiu.ac.bd.
hi i need dataset to download