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text (string)label (class label)
movie vara level la Erika poguthu
0 (Not_offensive)
I love Ajith Kumar Vivegam movie inki mjy bht achi lgi
5 (not-Tamil)
Padam nalla comedy padama irukum polaye..
0 (Not_offensive)
karthick subburaj anne .... intha padam vetri adaya unagalukku ennudaya valthukkal...
0 (Not_offensive)
கவுண்டர் தேவர்.சார்பாக வெற்றி பெற வாழ்த்துக்கள் 🦁
0 (Not_offensive)
ippo intha trailer ah parkuravana oru like podunga
0 (Not_offensive)
En thalaivan yogi babu irukkaar. Padam vera level laa irukkum
0 (Not_offensive)
Nerkonda parvai... Sema sema sema trailer
0 (Not_offensive)
ஏய் இது 96 யார் ஏமாத்தறீங்க? செம பின்னிட்டீங்க
0 (Not_offensive)
படம் கண்டிப்பாக வெற்றி பெற வேண்டும் செம்ம vara level
0 (Not_offensive)
Correct. Enga apa military da oodi vilaiyada solli tharuvaaaayaa
4 (Offensive_Targeted_Insult_Other)
Dislike pottavan ratham khaki dha savan
0 (Not_offensive)
ANANDHI ARMY HIT LIKE!! Ennayum ipdi aakitingale Anandi... #AnbukkuNanAdimai
0 (Not_offensive)
Thalaivaa mass nadipin nayagan surya anna fans hits like
0 (Not_offensive)
இந்த படம் கண்டிப்பாக வெற்றியடைய வேண்டும் என வாழ்த்துகிறேன்....
0 (Not_offensive)
Hit like if anybody laughed during this 1.06 .... Thaamasuuuu!!
0 (Not_offensive)
I Love vijay Anna, u r so cute anna
0 (Not_offensive)
Dei Rajini pavam da ne varaven poraven ellam karithuppatha koraiya pesittu erukkan 1:58
3 (Offensive_Targeted_Insult_Group)
செட்டியார் இன மக்கள் சார்பாக எனது சொந்த செலவில் 100 டிக்கெட் இலவசமாக வழங்குகிறேன். படக்குழுவினர்க்கு எனது மனமார்ந்த வாழ்த்துக்கள்
0 (Not_offensive)
Dhanush fans hit like here.....
0 (Not_offensive)
Dey dey deyyy,, loosu pasangala,, munna pinna jayalalitha amma va pathrukingalada gambeeramna ennanu avanga kannula tha paakanum,, amma oda history ah ithu,, soniya gandhi mari iruku chaiiii,,, avanga bio edukanumna a-z pathutu vanthu edungadaa,, dummy akkaathinga
3 (Offensive_Targeted_Insult_Group)
aathii yenna pa different aah eruku sama
0 (Not_offensive)
Viswasam teaser Ku aprm yarllam inga vanteenga?
0 (Not_offensive)
Intha maari comments ku like kekuravangala india va vittu veliya annupanum
1 (Offensive_Untargetede)
250 k likes ineram sila arivuketta pundaika ithku karanam vivek obrai ila kajal ila aksara kakathanu innum sollala?
1 (Offensive_Untargetede)
Ellam okay than....but antha ponnu aasa pattu love panni avanaiye kola panna thappu illaya...yosinga............
0 (Not_offensive)
Short film ah yum vittu vakalaya... Enada panni vachirukeenga
1 (Offensive_Untargetede)
Super i am waiting
0 (Not_offensive)
Any Nani and Surya mutual fans?
0 (Not_offensive)
தமிழ் நாட்ல இருக்குற சாதிய பைத்தியகார கூதியானுங்கள எல்லாம் ஓரே இடத்தில் பார்க்க ஒரு வாய்ப்பை தந்த மோகன் வன்னிக்கு சாரி பன்னிக்கு நன்றிகள்
3 (Offensive_Targeted_Insult_Group)
അണ്ണ പൊളി 259 k likes in 4hours ....
5 (not-Tamil)
Kekka bekka short film mathiri iruku
0 (Not_offensive)
في قناتي على اليوتيوب ( كل ماتتمناه 18 ) ًاشتركو في قناتي فضلً منكم ولا يسى امرا
5 (not-Tamil)
உங்க எல்லோரையும் பா. ரஞ்சித் எப்படி எல்லாம் கதர விட்டு இருக்காருன்னு இப்ப தான் புரியுது. அட்டகத்தி, மெட்ராஸ், பரியேறும் பெருமாள் இன்னும் எத்தனையோ படம் இன்னும் வர போகுது அதுக்கு எல்லோரும் மனச தேர்தி கொள்ளுங்க நண்பா
0 (Not_offensive)
Hit Like If U Felt Watching #Sarkar Movie
0 (Not_offensive)
Intha padamavuthu s.k ku nalla come back ah irukanum...
0 (Not_offensive)
Dance Illa p. Deva vai paarkave mudiyadhu pola
0 (Not_offensive)
Love scenes la Vera level thalaivar,
0 (Not_offensive)
இந்த படம் மாபெரும் வெற்றியடையும். #கமல்ஹாசன் ரசிகா்களின் வாழ்த்துக்கள்
0 (Not_offensive)
STR MASSSSSSSSSSSSSSS #ENIMEH NAMBA THAN DA
0 (Not_offensive)
Otha vaaya odachiruven thalapathiya pathu sirichirukka
1 (Offensive_Untargetede)
Abe sale ye toh tatti hai
5 (not-Tamil)
B- Bhayakaramana A- Ajith I- In R-Rasigar A-Admitting V-Vijay A-Anna A-Awesome <3
0 (Not_offensive)
Just 10 years apadiye elarum patha mathiri irukinga 🥳 padam vetriyadaya Vazthukal 🥳🥳🤩🤩
0 (Not_offensive)
Die Hard #Thala Fans Hit LIKE...
0 (Not_offensive)
மணிரத்னம்,AR ரஹ்மான், வைரமுத்து, சந்தோஷ் சிவன் தமிழ் சினிமா ல இல்லல்ல இந்திய சினிமால யே கால் நூற்றாண்டுகளா(25 வருசம்) ஒரே team இல்லல்ல உண்மையான நண்பர்களா இருந்துகிட்டு இருக்காய்ங்கே னா அது இந்த team தான் படம் எப்ப வரும் னு எதிர்பார்ப்பா இருக்கு.
0 (Not_offensive)
Singam Sinletha Varum BAIRAVAA Baikkiramma Varum
0 (Not_offensive)
Super semma mass from vijay fan
0 (Not_offensive)
நாடகக் காதல்..... குடும்பத்தோட பொண்ணு வீட்டுக்கு போய், வசதி வாய்ப்பை பார்த்து எவ்ளோ பணம் தேறும், நகை தேரும், சொத்து பத்து தேறும்னு கணக்கு போட்டு கல்யானம் பண்ணி, ரெண்டு நாளோ மூனு நாளோ பார்த்தவன்/வ கூட அன்னிக்கி நைட்டே படுக்கறதுக்கு பேரு தான் நாடகக் காதல்.
0 (Not_offensive)
வெற்றி பெற வாழ்த்துக்கள் கண்டிப்பாக திரையரங்கில் பார்க்கவேண்டும்
0 (Not_offensive)
Enagada traileriye verithanama yeduthurukinga...apo padam....screen kiliya poguthu...semmaaaaaaaa thalaivaa
0 (Not_offensive)
8000 dislike people should die as soon as posiible
1 (Offensive_Untargetede)
இது சாதி படம் அல்ல பெண்களின் படம் ஒரு சில பேர் பண்ணும் தவறால் சாதிக்கு கெட்ட பேரு அதான் படம்
0 (Not_offensive)
ஜெயம்ரவி ௨ங்கமேல நல்ல மறியாதை இருக்கு தலைவரை நிங்கள்ளும்மா
0 (Not_offensive)
வெற்றி பெற வாழ்த்துக்கள்... வெள்ளாளர் சார்பாக
0 (Not_offensive)
Marana bangam...Verithanam Overloaded to Surya Anna Fans...Hmm kola Mass pannuga Pangaligala...
0 (Not_offensive)
Goosebumps vantha vangala la like podungo
0 (Not_offensive)
Intha padam varanum samuga matrathirkana padam ...18k dislikes ithula ena theriuthu avlo poriki porikitu iruku ponnungala...
0 (Not_offensive)
MARVEL MEDIA MY CHANNEL. Pls visit subscribe
0 (Not_offensive)
Sirappana tharamana sambavatha inimel dha paakka porom
0 (Not_offensive)
0.41 look at the name tag vijay kumar
0 (Not_offensive)
#திரௌபதி சாதி வெறி படம்...சிறுத்தைகள் அப்ப ப.ரஞ்சித் எடுத்தது என்ன #A படமாடா நாயே🤣
0 (Not_offensive)
அன்றைக்கு எங்கள் குல சாமி சொன்னது திரௌபதி திரைப்படம் இவண்.மாவீரன் தம்பி சரத்...🦁🦁🦁
0 (Not_offensive)
Both VCK & PMK Fraudu pasanga than da En da itha padathula kaati engala makkala Jaathi kalavaram thoonduringa
0 (Not_offensive)
Manasu kulla irrundha kovam lam ippa வெட்ட வெளிச்சம் ஆகியிருக்கு .. நன்றி படக்குழு
0 (Not_offensive)
You kena tunggu lebih lama lagi untuk tahu saya! Who heard this Malaysians?
5 (not-Tamil)
Theatre ellaam insure panni vachukkangappaa.. 2.00 la theatre ellaam kandam!
1 (Offensive_Untargetede)
Naaa arasiyalukku varuvathu uruthi.....Rajini tells this dialog since 96. Semme joke.
2 (Offensive_Targeted_Insult_Individual)
Thalaivar vera level Suriya ennoda uyir Avaru oru rockstar
0 (Not_offensive)
Wow wow wow.... suriya Anna
0 (Not_offensive)
Comali 9looks teaser la varave Ella
3 (Offensive_Targeted_Insult_Group)
அருமை.... 10000000%சிறப்பு........ வாழ்க. குடும்பத்தினரோடு சென்று திரையரங்கில் மட்டுமே பார்ப்போம். இப்படிக்கு தேவர் மகன்.
0 (Not_offensive)
Jimmki kammal has more views 🤣
0 (Not_offensive)
U1 BGM massssssss verala level thala look semaaaaaa
0 (Not_offensive)
வாழ்த்துக்கள் நண்பா... சமூகத்தின் நடக்கும் உண்மை சம்பவங்களை படமாக எடுத்ததற்கு நெஞ்சார்ந்த நன்றியைத் தெரிவித்துக்கொள்கிறேன்.. இது போன்ற படங்களை மென் மேலும் படைக்க வாழ்த்துக்கள்...
0 (Not_offensive)
Mani Ratnam kitta ippdi oru script huh... Omg... Vera level!!(
0 (Not_offensive)
panggaaligala 2 million likes varanum innum vegama like ah therika vidunka
0 (Not_offensive)
Ayyyo thalaialva Vera lvl confirm ellaflrume RAJINIFIED
0 (Not_offensive)
Atlee verumpaya illa. Trailer layae evlo azhaga Katha soldraan!!!
0 (Not_offensive)
Pa Ranjith paru Da unaku sc St quote job government job quarters ellam cancel pannanum
2 (Offensive_Targeted_Insult_Individual)
Kajal Agarwal is back I so happy
0 (Not_offensive)
Vijay sethupathy sir saggatamizanpadam edutha eppti odama iruku I am waiting makkal selvan🤩🤩🤩🤩🤩🤩🤩
0 (Not_offensive)
Kadasi word its super (oruthavanga mela visuvasatha katrathukaga innoruthavangala asinga paduthathinga).
0 (Not_offensive)
thalaiva tharu maru marana mass vilayadalama
0 (Not_offensive)
2.04 .Rajini ah serupala adicha Maari irundhuchu
2 (Offensive_Targeted_Insult_Individual)
SK kagha paaka vandhavangha like pannungha
0 (Not_offensive)
But graphics sothapal panitanuga English Kara pottainga athan Padam late ayirichi
0 (Not_offensive)
அரசியலுக்கு வருவது உறுதி ... ரஜினி .... இது 1996 ... 🤣🤣🤣
0 (Not_offensive)
Anyone after NGK n Kaappaan teaser
0 (Not_offensive)
Friend nu FB la Like Kudukkara Friend dunu nenachiya life be kudukkanu da
3 (Offensive_Targeted_Insult_Group)
Ella scene summa therikuthu Kandipa veraleval blockbuster
0 (Not_offensive)
Gujarath patel inathin sarbaga vetri pera valthukkal
0 (Not_offensive)
unaku yaravathu palaya friendu irukangala...... nambatha....
0 (Not_offensive)
Statue odaikalam but b.r. kolkai alika mudiyathu
1 (Offensive_Untargetede)
Surya anna huge fan from #mangalore #coastal_Karnataka #tulunadu
0 (Not_offensive)
Please telugu dubbed telugu danush fans
0 (Not_offensive)
Iron man fans dis like podunga intha vidio kku
4 (Offensive_Targeted_Insult_Other)
Kuch samhj to nahi aaya but rajni did not needs any luggange he always rocks
5 (not-Tamil)
pakathane pora Intha kaliyoda attatha 🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘
1 (Offensive_Untargetede)
Marana mass ah irukke...song um trailer um...saavadi
1 (Offensive_Untargetede)
End of preview (truncated to 100 rows)

Dataset Card for Offenseval Dravidian

Dataset Summary

Offensive language identification is classification task in natural language processing (NLP) where the aim is to moderate and minimise offensive content in social media. It has been an active area of research in both academia and industry for the past two decades. There is an increasing demand for offensive language identification on social media texts which are largely code-mixed. Code-mixing is a prevalent phenomenon in a multilingual community and the code-mixed texts are sometimes written in non-native scripts. Systems trained on monolingual data fail on code-mixed data due to the complexity of code-switching at different linguistic levels in the text. This shared task presents a new gold standard corpus for offensive language identification of code-mixed text in Dravidian languages (Tamil-English, Malayalam-English, and Kannada-English).

Supported Tasks and Leaderboards

The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. The comment/post may contain more than one sentence but the average sentence length of the corpora is 1. Each comment/post is annotated at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios.

Languages

Code-mixed text in Dravidian languages (Tamil-English, Malayalam-English, and Kannada-English).

Dataset Structure

Data Instances

An example from the Tamil dataset looks as follows:

text label
படம் கண்டிப்பாக வெற்றி பெற வேண்டும் செம்ம vara level Not_offensive
Avasara patutiya editor uhh antha bullet sequence aa nee soliruka kudathu, athu sollama iruntha movie ku konjam support aa surprise element aa irunthurukum Not_offensive

An example from the Malayalam dataset looks as follows:

text label
ഷൈലോക്ക് ന്റെ നല്ല ടീസർ ആയിട്ട് പോലും ട്രോളി നടന്ന ലാലേട്ടൻ ഫാൻസിന് കിട്ടിയൊരു നല്ലൊരു തിരിച്ചടി തന്നെ ആയിരിന്നു ബിഗ് ബ്രദർ ന്റെ ട്രെയ്‌ലർ Not_offensive
Marana mass Ekka kku kodukku oru Not_offensive

An example from the Kannada dataset looks as follows:

text label
ನಿಜವಾಗಿಯೂ ಅದ್ಭುತ heartly heltidini... plz avrigella namma nimmellara supprt beku Not_offensive
Next song gu kuda alru andre evaga yar comment madidera alla alrru like madi share madi nam industry na next level ge togond hogaona. Not_offensive

Data Fields

Tamil

  • text: Tamil-English code mixed comment.
  • label: integer from 0 to 5 that corresponds to these values: "Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-Tamil"

Malayalam

  • text: Malayalam-English code mixed comment.
  • label: integer from 0 to 5 that corresponds to these values: "Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-malayalam"

Kannada

  • text: Kannada-English code mixed comment.
  • label: integer from 0 to 5 that corresponds to these values: "Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-Kannada"

Data Splits

train validation
Tamil 35139 4388
Malayalam 16010 1999
Kannada 6217 777

Dataset Creation

Curation Rationale

There is an increasing demand for offensive language identification on social media texts which are largely code-mixed. Code-mixing is a prevalent phenomenon in a multilingual community and the code-mixed texts are sometimes written in non-native scripts. Systems trained on monolingual data fail on code-mixed data due to the complexity of code-switching at different linguistic levels in the text.

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

Youtube users

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

This work is licensed under a Creative Commons Attribution 4.0 International Licence

Citation Information

@article{chakravarthi-etal-2021-lre,
title = "DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text",
author = "Chakravarthi, Bharathi Raja  and
  Priyadharshini, Ruba  and
  Muralidaran, Vigneshwaran and
  Jose, Navya and
  Suryawanshi, Shardul and
  Sherly, Elizabeth  and
  McCrae, John P",
  journal={Language Resources and Evaluation},
  publisher={Springer}
}
@inproceedings{dravidianoffensive-eacl,
title={Findings of the Shared Task on {O}ffensive {L}anguage {I}dentification in {T}amil, {M}alayalam, and {K}annada},
author={Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Jose, Navya and
M, Anand Kumar and
Mandl, Thomas and
Kumaresan, Prasanna Kumar and
Ponnsamy, Rahul and
V,Hariharan and
Sherly, Elizabeth and
McCrae, John Philip },
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = April,
year = "2021",
publisher = "Association for Computational Linguistics",
year={2021}
}
@inproceedings{hande-etal-2020-kancmd,
    title = "{K}an{CMD}: {K}annada {C}ode{M}ixed Dataset for Sentiment Analysis and Offensive Language Detection",
    author = "Hande, Adeep  and
      Priyadharshini, Ruba  and
      Chakravarthi, Bharathi Raja",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.6",
    pages = "54--63",
    abstract = "We introduce Kannada CodeMixed Dataset (KanCMD), a multi-task learning dataset for sentiment analysis and offensive language identification. The KanCMD dataset highlights two real-world issues from the social media text. First, it contains actual comments in code mixed text posted by users on YouTube social media, rather than in monolingual text from the textbook. Second, it has been annotated for two tasks, namely sentiment analysis and offensive language detection for under-resourced Kannada language. Hence, KanCMD is meant to stimulate research in under-resourced Kannada language on real-world code-mixed social media text and multi-task learning. KanCMD was obtained by crawling the YouTube, and a minimum of three annotators annotates each comment. We release KanCMD 7,671 comments for multitask learning research purpose.",
}
@inproceedings{chakravarthi-etal-2020-corpus,
    title = "Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text",
    author = "Chakravarthi, Bharathi Raja  and
      Muralidaran, Vigneshwaran  and
      Priyadharshini, Ruba  and
      McCrae, John Philip",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.28",
    pages = "202--210",
    abstract = "Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{chakravarthi-etal-2020-sentiment,
    title = "A Sentiment Analysis Dataset for Code-Mixed {M}alayalam-{E}nglish",
    author = "Chakravarthi, Bharathi Raja  and
      Jose, Navya  and
      Suryawanshi, Shardul  and
      Sherly, Elizabeth  and
      McCrae, John Philip",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.25",
    pages = "177--184",
    abstract = "There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff{'}s alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}

Contributions

Thanks to @jamespaultg for adding this dataset.

Update on GitHub