The dataset viewer is not available for this dataset.
Error code: DatasetModuleNotInstalledError
Exception: ImportError
Message: To be able to use SarcasmNet/sarcasm, you need to install the following dependencies: geopandas, matplotlib, seaborn, shapely.
Please install them using 'pip install geopandas matplotlib seaborn shapely' for instance.
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
config_names = get_dataset_config_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1879, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1845, in dataset_module_factory
return HubDatasetModuleFactoryWithScript(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1473, in get_module
local_imports = _download_additional_modules(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 349, in _download_additional_modules
raise ImportError(
ImportError: To be able to use SarcasmNet/sarcasm, you need to install the following dependencies: geopandas, matplotlib, seaborn, shapely.
Please install them using 'pip install geopandas matplotlib seaborn shapely' for instance.Need help to make the dataset viewer work? Open a discussion for direct support.
Dataset Card for Sarcasm Detection Dataset
Dataset Details
Dataset Description
The Sarcasm Detection Dataset is designed for identifying instances of sarcasm in text. The dataset aims to address difficulties in sarcasm detection due to the subjective and contextual nature of language.
Uses
Direct Use
The dataset can be used for training machine learning models to detect sarcasm in text, which has applications in sentiment analysis, social media monitoring, and natural language understanding tasks.
Dataset Structure
The dataset consists of text examples labeled as sarcastic or non-sarcastic. Each example is accompanied by metadata indicating sarcasm markers and linguistic patterns.
Dataset Creation
Curation Rationale
The dataset was curated to provide a diverse collection of sarcastic and non-sarcastic text examples, aiming to capture the complexities of sarcasm in natural language.
Source Data
Data Collection and Processing
The data collection process involved sourcing text samples from various sources, including social media, online forums, and news articles. Each sample was manually annotated as sarcastic or non-sarcastic by human annotators.
Annotations [optional]
Annotation process
Annotations were performed by human annotators who were provided with guidelines for identifying sarcasm in text. Interannotator agreement was measured to ensure consistency in labeling.
Bias, Risks, and Limitations
The dataset may contain biases inherent in the selection and annotation process, including cultural biases and subjective interpretations of sarcasm.
Recommendations
Users are advised to consider the limitations of the dataset when training and evaluating sarcasm detection models.
Citation [optional]
Khodak, M., Saunshi, N., & Vodrahalli, K. (2018). A Large Self-Annotated Corpus for Sarcasm. In LREC 2018 (pp. 1-6). Rahman M O, Hossain M S, Junaid T S, et al. Predicting prices of stock market using gated recurrent units (GRUs) neural networks[J]. Int. J. Comput. Sci. Netw. Secur, 2019, 19(1): 213-222. Yu Y, Si X, Hu C, et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural computation, 2019, 31(7): 1235-1270. Gole, M., Nwadiugwu, W. P., & Miranskyy, A. (2023). On Sarcasm Detection with OpenAI GPT-based Models. B. Sonare, J. H. Dewan, S. D. Thepade, V. Dadape, T. Gadge and A. Gavali, "Detecting Sarcasm in Reddit Comments: A Comparative Analysis," 2023 4th International Conference for Emerging Technology (INCET), Belgaum, India, 2023, pp. 1-6, doi: 10.1109/INCET57972.2023.10170613.
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