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README.md

@@ -1,42 +1,70 @@
+
 ---
-license: unknown
-tags:
-- aspect-based-sentiment-analysis
-language:
+language: 
 - jav
 - sun
+pretty_name: Local Id Abusive
+task_categories: 
+- aspect-based-sentiment-analysis
+tags: 
+- aspect-based-sentiment-analysis
 ---
 
-# local_id_abusive
-
 This dataset is for abusive and hate speech detection, using Twitter text containing Javanese and Sundanese words.
 
-
-
 (from the publication source)
-
 The Indonesian local language dataset collection was conducted using Twitter search API to collect the tweets and then
-
 implemented using Tweepy Library. The tweets were collected using queries from the list of abusive words in Indonesian
-
 tweets. The abusive words were translated into local Indonesian languages, which are Javanese and Sundanese. The
-
 translated words are then used as queries to collect tweets containing Indonesian and local languages. The translation
-
 process involved native speakers for each local language. The crawling process has collected a total of more than 5000
-
 tweets. Then, the crawled data were filtered to get tweets that contain local’s vocabulary and/or sentences in Javanese
-
 and Sundanese. Next, after the filtering process, the data will be labeled whether the tweets are labeled as hate speech
-
 and abusive language or not.
 
+
+## Languages
+
+jav, sun
+
+## Supported Tasks
+
+Aspect Based Sentiment Analysis
+    
 ## Dataset Usage
+### Using `datasets` library
+```
+    from datasets import load_dataset
+    dset = datasets.load_dataset("SEACrowd/local_id_abusive", trust_remote_code=True)
+```
+### Using `seacrowd` library
+```import seacrowd as sc
+# Load the dataset using the default config
+    dset = sc.load_dataset("local_id_abusive", schema="seacrowd")
+# Check all available subsets (config names) of the dataset
+    print(sc.available_config_names("local_id_abusive"))
+# Load the dataset using a specific config
+    dset = sc.load_dataset_by_config_name(config_name="<config_name>")
+```
+    
+    More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use).
+    
+
+## Dataset Homepage
+
+[https://github.com/Shofianina/local-indonesian-abusive-hate-speech-dataset](https://github.com/Shofianina/local-indonesian-abusive-hate-speech-dataset)
+
+## Dataset Version
+
+Source: 1.0.0. SEACrowd: 2024.06.20.
 
-Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
+## Dataset License
+
+Unknown
 
 ## Citation
 
+If you are using the **Local Id Abusive** dataloader in your work, please cite the following:
 ```
 @inproceedings{putri2021abusive,
   title={Abusive language and hate speech detection for Javanese and Sundanese languages in tweets: Dataset and preliminary study},
@@ -47,16 +75,14 @@ Run `pip install nusacrowd` before loading the dataset through HuggingFace's `lo
   organization={International Workshop on Computer Science and Engineering (WCSE)},
   abstract={Indonesia’s demography as an archipelago with lots of tribes and local languages added variances in their communication style. Every region in Indonesia has its own distinct culture, accents, and languages. The demographical condition can influence the characteristic of the language used in social media, such as Twitter. It can be found that Indonesian uses their own local language for communicating and expressing their mind in tweets. Nowadays, research about identifying hate speech and abusive language has become an attractive and developing topic. Moreover, the research related to Indonesian local languages still rarely encountered. This paper analyzes the use of machine learning approaches such as Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest Decision Tree (RFDT) in detecting hate speech and abusive language in Sundanese and Javanese as Indonesian local languages. The classifiers were used with the several term weightings features, such as word n-grams and char n-grams. The experiments are evaluated using the F-measure. It achieves over 60 % for both local languages.}
 }
-```
 
-## License
-
-Unknown
 
-## Homepage
-
-[https://github.com/Shofianina/local-indonesian-abusive-hate-speech-dataset](https://github.com/Shofianina/local-indonesian-abusive-hate-speech-dataset)
-
-### NusaCatalogue
+@article{lovenia2024seacrowd,
+    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, 
+    author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
+    year={2024},
+    eprint={2406.10118},
+    journal={arXiv preprint arXiv: 2406.10118}
+}
 
-For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
+```