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import logging |
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from typing import TypedDict |
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import datasets as ds |
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import pandas as pd |
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logger = logging.getLogger(__name__) |
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_CITATION = """\ |
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@inproceedings{kajiwara-etal-2021-wrime, |
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title = "{WRIME}: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations", |
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author = "Kajiwara, Tomoyuki and |
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Chu, Chenhui and |
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Takemura, Noriko and |
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Nakashima, Yuta and |
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Nagahara, Hajime", |
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booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", |
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month = jun, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.naacl-main.169", |
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doi = "10.18653/v1/2021.naacl-main.169", |
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pages = "2095--2104", |
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abstract = "We annotate 17,000 SNS posts with both the writer{'}s subjective emotional intensity and the reader{'}s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer{'}s subjective labels than the readers{'}. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.", |
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} |
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""" |
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_DESCRIPTION = """\ |
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WRIME dataset is a new dataset for emotional intensity estimation with subjective and objective annotations. |
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""" |
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_HOMEPAGE = "https://github.com/ids-cv/wrime" |
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_LICENSE = """\ |
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- The dataset is available for research purposes only. |
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- Redistribution of the dataset is prohibited. |
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""" |
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class URLs(TypedDict): |
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ver1: str |
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ver2: str |
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_URLS: URLs = { |
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"ver1": "https://raw.githubusercontent.com/ids-cv/wrime/master/wrime-ver1.tsv", |
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"ver2": "https://raw.githubusercontent.com/ids-cv/wrime/master/wrime-ver2.tsv", |
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} |
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def _fix_typo_in_dataset(df: pd.DataFrame) -> pd.DataFrame: |
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df = df.rename( |
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columns={ |
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"Reader2_Saddness": "Reader2_Sadness", |
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"Reader3_Saddness": "Reader3_Sadness", |
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} |
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) |
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return df |
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def _load_tsv(tsv_path: str) -> pd.DataFrame: |
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logger.info(f"Load TSV file from {tsv_path}") |
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df = pd.read_csv(tsv_path, delimiter="\t") |
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df = _fix_typo_in_dataset(df) |
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return df |
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class WrimeDataset(ds.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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ds.BuilderConfig( |
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name="ver1", |
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version=ds.Version("1.0.0"), |
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description="WRIME dataset ver. 1", |
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), |
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ds.BuilderConfig( |
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name="ver2", |
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version=ds.Version("2.0.0"), |
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description="WRIME dataset ver. 2", |
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), |
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] |
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def _info(self) -> ds.DatasetInfo: |
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features = ds.Features( |
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{ |
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"sentence": ds.Value("string"), |
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"user_id": ds.Value("string"), |
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"datetime": ds.Value("string"), |
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"writer": { |
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"joy": ds.Value("uint8"), |
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"sadness": ds.Value("uint8"), |
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"anticipation": ds.Value("uint8"), |
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"surprise": ds.Value("uint8"), |
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"anger": ds.Value("uint8"), |
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"fear": ds.Value("uint8"), |
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"disgust": ds.Value("uint8"), |
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"trust": ds.Value("uint8"), |
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}, |
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"reader1": { |
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"joy": ds.Value("uint8"), |
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"sadness": ds.Value("uint8"), |
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"anticipation": ds.Value("uint8"), |
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"surprise": ds.Value("uint8"), |
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"anger": ds.Value("uint8"), |
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"fear": ds.Value("uint8"), |
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"disgust": ds.Value("uint8"), |
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"trust": ds.Value("uint8"), |
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}, |
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"reader2": { |
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"joy": ds.Value("uint8"), |
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"sadness": ds.Value("uint8"), |
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"anticipation": ds.Value("uint8"), |
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"surprise": ds.Value("uint8"), |
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"anger": ds.Value("uint8"), |
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"fear": ds.Value("uint8"), |
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"disgust": ds.Value("uint8"), |
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"trust": ds.Value("uint8"), |
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}, |
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"reader3": { |
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"joy": ds.Value("uint8"), |
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"sadness": ds.Value("uint8"), |
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"anticipation": ds.Value("uint8"), |
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"surprise": ds.Value("uint8"), |
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"anger": ds.Value("uint8"), |
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"fear": ds.Value("uint8"), |
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"disgust": ds.Value("uint8"), |
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"trust": ds.Value("uint8"), |
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}, |
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"avg_readers": { |
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"joy": ds.Value("uint8"), |
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"sadness": ds.Value("uint8"), |
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"anticipation": ds.Value("uint8"), |
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"surprise": ds.Value("uint8"), |
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"anger": ds.Value("uint8"), |
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"fear": ds.Value("uint8"), |
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"disgust": ds.Value("uint8"), |
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"trust": ds.Value("uint8"), |
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}, |
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} |
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) |
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return ds.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: ds.DownloadManager): |
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wrime_datasets = dl_manager.download_and_extract(_URLS) |
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major_version_name = f"ver{self.config.version.major}" |
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wrime_df = _load_tsv(tsv_path=wrime_datasets[major_version_name]) |
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tng_wrime_df = wrime_df[wrime_df["Train/Dev/Test"] == "train"] |
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dev_wrime_df = wrime_df[wrime_df["Train/Dev/Test"] == "dev"] |
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tst_wrime_df = wrime_df[wrime_df["Train/Dev/Test"] == "test"] |
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return [ |
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ds.SplitGenerator( |
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name=ds.Split.TRAIN, |
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gen_kwargs={"df": tng_wrime_df}, |
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), |
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ds.SplitGenerator( |
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name=ds.Split.VALIDATION, |
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gen_kwargs={"df": dev_wrime_df}, |
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), |
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ds.SplitGenerator( |
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name=ds.Split.TEST, |
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gen_kwargs={"df": tst_wrime_df}, |
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), |
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] |
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def _generate_examples( |
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self, |
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df: pd.DataFrame, |
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): |
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for i in range(len(df)): |
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row_df = df.iloc[i] |
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example_dict = { |
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"sentence": row_df["Sentence"], |
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"user_id": row_df["UserID"], |
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"datetime": row_df["Datetime"], |
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} |
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example_dict["writer"] = { |
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"joy": row_df["Writer_Joy"], |
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"sadness": row_df["Writer_Sadness"], |
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"anticipation": row_df["Writer_Anticipation"], |
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"surprise": row_df["Writer_Surprise"], |
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"anger": row_df["Writer_Anger"], |
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"fear": row_df["Writer_Fear"], |
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"disgust": row_df["Writer_Disgust"], |
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"trust": row_df["Writer_Trust"], |
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} |
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for reader_num in range(1, 4): |
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example_dict[f"reader{reader_num}"] = { |
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"joy": row_df[f"Reader{reader_num}_Joy"], |
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"sadness": row_df[f"Reader{reader_num}_Sadness"], |
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"anticipation": row_df[f"Reader{reader_num}_Anticipation"], |
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"surprise": row_df[f"Reader{reader_num}_Surprise"], |
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"anger": row_df[f"Reader{reader_num}_Anger"], |
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"fear": row_df[f"Reader{reader_num}_Fear"], |
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"disgust": row_df[f"Reader{reader_num}_Disgust"], |
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"trust": row_df[f"Reader{reader_num}_Trust"], |
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} |
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example_dict["avg_readers"] = { |
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"joy": row_df["Avg. Readers_Joy"], |
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"sadness": row_df["Avg. Readers_Sadness"], |
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"anticipation": row_df["Avg. Readers_Anticipation"], |
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"surprise": row_df["Avg. Readers_Surprise"], |
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"anger": row_df["Avg. Readers_Anger"], |
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"fear": row_df["Avg. Readers_Fear"], |
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"disgust": row_df["Avg. Readers_Disgust"], |
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"trust": row_df["Avg. Readers_Trust"], |
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} |
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yield i, example_dict |
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