Create emotion.py

emotion.py

@@ -0,0 +1,88 @@
+import json
+
+import datasets
+from datasets.tasks import TextClassification
+
+
+_CITATION = """\
+@inproceedings{saravia-etal-2018-carer,
+    title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
+    author = "Saravia, Elvis  and
+      Liu, Hsien-Chi Toby  and
+      Huang, Yen-Hao  and
+      Wu, Junlin  and
+      Chen, Yi-Shin",
+    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
+    month = oct # "-" # nov,
+    year = "2018",
+    address = "Brussels, Belgium",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/D18-1404",
+    doi = "10.18653/v1/D18-1404",
+    pages = "3687--3697",
+    abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
+}
+"""
+
+_DESCRIPTION = """\
+Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.
+"""
+
+_HOMEPAGE = "https://github.com/dair-ai/emotion_dataset"
+
+_LICENSE = "The dataset should be used for educational and research purposes only"
+
+_URLS = {
+    "split": {
+        "train": "data/train.jsonl.gz",
+        "validation": "data/validation.jsonl.gz",
+        "test": "data/test.jsonl.gz",
+    },
+    "unsplit": {
+        "train": "data/data.jsonl.gz",
+    },
+}
+
+
+class Emotion(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="split", version=VERSION, description="Dataset split in train, validation and test"
+        ),
+        datasets.BuilderConfig(name="unsplit", version=VERSION, description="Unsplit dataset"),
+    ]
+    DEFAULT_CONFIG_NAME = "split"
+
+    def _info(self):
+        class_names = ["sadness", "joy", "love", "anger", "fear", "surprise"]
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {"text": datasets.Value("string"), "label": datasets.ClassLabel(names=class_names)}
+            ),
+            supervised_keys=("text", "label"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+            task_templates=[TextClassification(text_column="text", label_column="label")],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        paths = dl_manager.download_and_extract(_URLS[self.config.name])
+        if self.config.name == "split":
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": paths["train"]}),
+                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": paths["validation"]}),
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": paths["test"]}),
+            ]
+        else:
+            return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": paths["train"]})]
+
+    def _generate_examples(self, filepath):
+        """Generate examples."""
+        with open(filepath, encoding="utf-8") as f:
+            for idx, line in enumerate(f):
+                example = json.loads(line)
+                yield idx, example