First commit

NaijaSenti-Twitter.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis"""
+
+
+
+_HOMEPAGE = "https://github.com/hausanlp/NaijaSenti"
+
+_DESCRIPTION = """\
+NaijaSenti is the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria — Hausa, Igbo, Nigerian-Pidgin, and Yorùbá — consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets.
+"""
+
+
+_CITATION = """\
+@inproceedings{muhammad-etal-2022-naijasenti,
+    title = "{N}aija{S}enti: A {N}igerian {T}witter Sentiment Corpus for Multilingual Sentiment Analysis",
+    author = "Muhammad, Shamsuddeen Hassan  and
+      Adelani, David Ifeoluwa  and
+      Ruder, Sebastian  and
+      Ahmad, Ibrahim Sa{'}id  and
+      Abdulmumin, Idris  and
+      Bello, Bello Shehu  and
+      Choudhury, Monojit  and
+      Emezue, Chris Chinenye  and
+      Abdullahi, Saheed Salahudeen  and
+      Aremu, Anuoluwapo  and
+      Jorge, Al{\'\i}pio  and
+      Brazdil, Pavel",
+    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
+    month = jun,
+    year = "2022",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://aclanthology.org/2022.lrec-1.63",
+    pages = "590--602",
+}
+"""
+
+
+import csv
+import textwrap
+import pandas as pd
+
+import datasets
+
+LANGUAGES = ['hau', 'ibo', 'yor', 'pcm']
+
+class NaijaSentiConfig(datasets.BuilderConfig):
+    """BuilderConfig for NaijaSenti"""
+
+    def __init__(
+        self,
+        text_features,
+        label_column,
+        label_classes,
+        train_url,
+        valid_url,
+        test_url,
+        citation,
+        **kwargs,
+    ):
+        """BuilderConfig for NaijaSenti.
+
+        Args:
+          text_features: `dict[string]`, map from the name of the feature
+            dict for each text field to the name of the column in the txt/csv/tsv file
+          label_column: `string`, name of the column in the txt/csv/tsv file corresponding
+            to the label
+          label_classes: `list[string]`, the list of classes if the label is categorical
+          train_url: `string`, url to train file from
+          valid_url: `string`, url to valid file from
+          test_url: `string`, url to test file from
+          citation: `string`, citation for the data set
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(NaijaSentiConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+        self.text_features = text_features
+        self.label_column = label_column
+        self.label_classes = label_classes
+        self.train_url = train_url
+        self.valid_url = valid_url
+        self.test_url = test_url
+        self.citation = citation
+
+
+class NaijaSenti(datasets.GeneratorBasedBuilder):
+    """NaijaSenti benchmark"""
+
+    BUILDER_CONFIGS = []
+
+    for lang in LANGUAGES:
+        BUILDER_CONFIGS.append(
+            NaijaSentiConfig(
+                name=lang,
+                description=textwrap.dedent(
+                    f"""\
+                    {lang} dataset."""
+                ),
+                text_features={"tweet": "tweet"},
+                label_classes=["positive", "neutral", "negative"],
+                label_column="label",
+                train_url=f"https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/data/annotated_tweets/{lang}/train.tsv",
+                valid_url=f"https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/data/annotated_tweets/{lang}/dev.tsv",
+                test_url=f"https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/data/annotated_tweets/{lang}/test.tsv",
+                citation=textwrap.dedent(
+                    f"""\
+                    {lang} citation"""
+                ),
+            ),
+        )
+
+    def _info(self):
+        features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features}
+        features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
+
+        return datasets.DatasetInfo(
+            description=self.config.description,
+            features=datasets.Features(features),
+            citation=self.config.citation,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        train_path = dl_manager.download_and_extract(self.config.train_url)
+        valid_path = dl_manager.download_and_extract(self.config.valid_url)
+        test_path = dl_manager.download_and_extract(self.config.test_url)
+        
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        df = pd.read_csv(filepath, sep='\t')
+
+        print('-'*100)
+        print(df.head())
+        print('-'*100)
+
+        for id_, row in df.iterrows():
+            tweet = row["tweet"]
+            label = row["label"]
+
+            yield id_, {"tweet": tweet, "label": label}

README.md

@@ -1,3 +1,219 @@
 ---
-license: cc-by-sa-4.0
+task_categories:
+- text-classification
+task_ids:
+- sentiment-analysis
+- sentiment-classification
+- sentiment-scoring
+- semantic-similarity-classification
+- semantic-similarity-scoring
+tags:
+- sentiment analysis, Twitter, tweets
+- sentiment
+multilinguality:
+- monolingual
+- multilingual
+size_categories:
+- 100K<n<1M
+language:
+- hau
+- ibo
+- pcm
+- yor
+pretty_name: AfriSenti
 ---
+
+<p align="center">
+<img src="https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/image/naijasenti_logo1.png", width="500">
+
+--------------------------------------------------------------------------------
+
+## Dataset Description
+
+
+- **Homepage:** https://github.com/hausanlp/NaijaSenti
+- **Repository:** [GitHub](https://github.com/hausanlp/NaijaSenti)
+- **Paper:**  [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://aclanthology.org/2022.lrec-1.63/)
+- **Leaderboard:** N/A
+- **Point of Contact:** [Shamsuddeen Hassan Muhammad](shamsuddeen2004@gmail.com)
+
+
+### Dataset Summary
+
+ NaijaSenti is the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria — Hausa, Igbo, Nigerian-Pidgin, and Yorùbá — consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets.
+
+
+### Supported Tasks and Leaderboards
+
+The NaijaSenti can be used for a wide range of sentiment analysis tasks in Nigerian languages, such as sentiment classification, sentiment intensity analysis, and emotion detection. This dataset is suitable for training and evaluating machine learning models for various NLP tasks related to sentiment analysis in African languages. It was part of the datasets that were used for [SemEval 2023 Task 12: Sentiment Analysis for African Languages](https://codalab.lisn.upsaclay.fr/competitions/7320).
+
+
+### Languages
+
+4 most spoken Nigerian languages 
+
+* Hausa (hau) 
+* Igbo (ibo)
+* Nigerian Pidgin (pcm)
+* Yoruba (yor) 
+
+
+## Dataset Structure
+
+### Data Instances
+
+For each instance, there is a string for the tweet and a string for the label. See the NaijaSenti [dataset viewer](https://huggingface.co/datasets/HausaNLP/NaijaSenti/viewer/HausaNLP--NaijaSenti/train) to explore more examples.
+
+
+```
+{
+  "tweet": "string",
+  "label": "string"
+}
+```
+
+
+### Data Fields
+
+The data fields are:
+
+```
+tweet: a string feature.
+label: a classification label, with possible values including positive, negative and neutral.
+```
+
+
+### Data Splits
+
+The NaijaSenti dataset has 3 splits: train, validation, and test. Below are the statistics for Version 1.0.0 of the dataset.
+
+|  | hau | ibo | pcm | yor |
+|---|---|---|---|---|
+| train | 14,172 | 10,192 | 5,121 | 8,522  |
+| dev | 2,677 | 1,841 | 1,281 | 2,090 |
+| test | 5,303 | 3,682 | 4,154 | 4,515 |
+| total | 22,152 | 15,715 | 10,556 | 15,127 |
+
+
+### How to use it
+
+
+```python
+from  datasets  import  load_dataset
+
+# you can load specific languages (e.g., Amharic). This download train, validation and test sets. 
+ds = load_dataset("HausaNLP/NaijaSenti-Twitter", "hau")
+
+# train set only
+ds = load_dataset("HausaNLP/NaijaSenti-Twitter", "hau", split = "train")
+
+# test set only
+ds = load_dataset("HausaNLP/NaijaSenti-Twitter", "hau", split = "test")
+
+# validation set only
+ds = load_dataset("HausaNLP/NaijaSenti-Twitter", "hau", split = "validation")
+
+```
+
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+NaijaSenti Version 1.0.0 aimed to be used sentiment analysis and other related task in Nigerian indigenous and creole languages - Hausa, Igbo, Nigerian Pidgin and Yoruba.
+
+
+### Source Data
+
+Twitter
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+We anonymized the tweets by replacing all *@mentions* by *@user* and removed all URLs.
+
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The NaijaSenti dataset has the potential to improve sentiment analysis for Nigerian languages, which is essential for understanding and analyzing the diverse perspectives of people in Nigeria. This dataset can enable researchers and developers to create sentiment analysis models that are specific to Nigerian languages, which can be used to gain insights into the social, cultural, and political views of people in Nigerian. Furthermore, this dataset can help address the issue of underrepresentation of Nigerian languages in natural language processing, paving the way for more equitable and inclusive AI technologies.
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+* Shamsuddeen Hassan Muhammad
+* Idris Abdulmumin
+* Ibrahim Said Ahmad
+* Bello Shehu Bello
+
+
+### Licensing Information
+
+This NaijaSenti is licensed under a Creative Commons Attribution BY-NC-SA 4.0 International License
+
+
+
+
+### Citation Information
+
+```
+@inproceedings{muhammad-etal-2022-naijasenti,
+    title = "{N}aija{S}enti: A {N}igerian {T}witter Sentiment Corpus for Multilingual Sentiment Analysis",
+    author = "Muhammad, Shamsuddeen Hassan  and
+      Adelani, David Ifeoluwa  and
+      Ruder, Sebastian  and
+      Ahmad, Ibrahim Sa{'}id  and
+      Abdulmumin, Idris  and
+      Bello, Bello Shehu  and
+      Choudhury, Monojit  and
+      Emezue, Chris Chinenye  and
+      Abdullahi, Saheed Salahudeen  and
+      Aremu, Anuoluwapo  and
+      Jorge, Al{\'\i}pio  and
+      Brazdil, Pavel",
+    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
+    month = jun,
+    year = "2022",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://aclanthology.org/2022.lrec-1.63",
+    pages = "590--602",
+}
+```
+
+
+### Contributions
+
+[More Information Needed]

dataset_infos.json

@@ -0,0 +1,57 @@
+{
+  "default": {
+    "description": "NaijaSenti is the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria — Hausa, Igbo, Nigerian-Pidgin, and Yorùbá — consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets.\n",
+    "citation": " ",
+    "homepage": "https://github.com/hausanlp/NaijaSenti",
+    "license": "",
+    "features": {
+      "text": {
+        "dtype": "string",
+        "id": null,
+        "_type": "Value"
+      },
+      "label": {
+        "num_classes": 3,
+        "names": [
+          "positive",
+          "negative",
+          "neutral"
+        ],
+        "names_file": null,
+        "id": null,
+        "_type": "ClassLabel"
+      }
+    },
+    "post_processed": null,
+    "supervised_keys": {
+      "input": "tweet",
+      "output": "label"
+    },
+    "task_templates": [
+      {
+        "task": "text-classification",
+        "text_column": "tweet",
+        "label_column": "label",
+        "labels": [
+          "positive",
+          "negative",
+          "neutral"
+        ]
+      }
+    ],
+    "builder_name": "NaijaSenti",
+    "config_name": "default",
+    "version": {
+      "version_str": "0.0.0",
+      "description": null,
+      "major": 0,
+      "minor": 0,
+      "patch": 0
+  
+    },
+    "download_size": 2069616,
+    "post_processing_size": null,
+    "dataset_size": 2173417,
+    "size_in_bytes": 4243033
+  }
+}