shawon95/Bengali-Fake-Review-Dataset
收藏数据集概述
数据集名称
- Bengali Fake Review Detection (BFRD) 数据集
数据集用途
- 用于检测孟加拉语假评论
数据集来源
- 该数据集是在论文《Bengali Fake Reviews: A Benchmark Dataset and Detection System》中提出的，该论文发表于Elsevier出版的《Neurocomputing》期刊。
数据集特点
- 由4名母语为孟加拉语的标注者进行标注，信任度评分超过90%。
- 使用Fleiss Kappa评分进行一致性评估，得分为0.83。
数据集统计
- 假评论：1339条
- 非假评论：7710条
数据集详细统计
| 统计项 | 假评论 | 非假评论 |
|---|---|---|
| 总字数 | 155,789 | 927,902 |
| 总唯一字数 | 17,739 | 51,200 |
| 最大评论长度 | 693 | 1,614 |
| 平均字数 | 116.35 | 120.35 |
| 平均唯一字数 | 84.99 | 88.42 |
引用信息
-
若使用此数据集，请引用以下论文：
@article{SHAHARIAR2024127732, title = {Bengali fake reviews: A benchmark dataset and detection system}, journal = {Neurocomputing}, pages = {127732}, year = {2024}, issn = {0925-2312}, doi = {https://doi.org/10.1016/j.neucom.2024.127732}, url = {https://www.sciencedirect.com/science/article/pii/S0925231224005034}, author = {G.M. Shahariar and Md. Tanvir Rouf Shawon and Faisal Muhammad Shah and Mohammad Shafiul Alam and Md. Shahriar Mahbub}, keywords = {Bengali fake reviews detection, Ensemble learning, Transformers, Deep learning, Augmentation, Transliteration}, abstract = {The proliferation of fake reviews on various online platforms has created a major concern for both consumers and businesses. Such reviews can deceive customers and cause damage to the reputation of products or services, making it crucial to identify them. Although the detection of fake reviews has been extensively studied in English language, detecting fake reviews in non-English languages such as Bengali is still a relatively unexplored research area. The novelty of the study unfolds on three fronts: (i) a new publicly available dataset called Bengali Fake Review Detection (BFRD) dataset is introduced, (ii) a unique pipeline has been proposed that translates English words to their corresponding Bengali meaning and also back transliterates Romanized Bengali to Bengali, (iii) a weighted ensemble model that combines four pre-trained transformers model is proposed. The developed dataset consists of 7710 non-fake and 1339 fake food-related reviews collected from social media posts. Rigorous experiments have been conducted to compare multiple deep learning and pre-trained transformer language models and our proposed model to identify the best-performing model. According to the experimental results, the proposed ensemble model attained a weighted F1-score of 0.9843 on a dataset of 13,390 reviews, comprising 1339 actual fake reviews, 5,356 augmented fake reviews, and 6695 reviews randomly selected from the 7710 non-fake instances.} }
