Datasets:

Task Categories: text-classification
Languages: en ta
Multilinguality: multilingual
Size Categories: 10K<n<100K
Licenses: unknown
Language Creators: crowdsourced
Annotations Creators: expert-generated
Source Datasets: original
Dataset Preview Go to dataset viewer
text (string)label (class label)
Trailer late ah parthavanga like podunga
0 (Positive)
Move pathutu vanthu trailer pakurvnga yaru
0 (Positive)
Puthupetai dhanush ah yarellam pathinga
0 (Positive)
Dhanush oda character ,puthu sa erukay , mass ta
0 (Positive)
vera level ippa pesungada mokka nu thalaivaaaaaa
0 (Positive)
Thala mass . U1 bgm. Vera level
0 (Positive)
ivara pathta death vadi madiri irukku
1 (Negative)
0:28 90's kids reference Maadila irunthu kudicha Shakthiman kaapathvaaru
0 (Positive)
Aiyoo thala mass pannita thala
0 (Positive)
ENPT ku kaathu kadanthathellam oru nimishathula poiruchu
2 (Mixed_feelings)
When Pandey gets up Vandumurugan : IM SPEAKING.. NO CROSS SPEAKING... SITTT DOWNNN
1 (Negative)
Remake of Malayalam Mohanlal movie Devadoodhan
1 (Negative)
Tharamaana sirappaana sambavam inimeal ta paaka pora....
0 (Positive)
Viswasam trailer paathutu Inka vanthavanga like panuga
3 (unknown_state)
Maran kattu kattitamnue verithanam thalivaaàa
0 (Positive)
Ella trailer la last la oru Mass dialouge vaichikuringaaa paaaa
0 (Positive)
Eppa Saamy thala padathula Vera director pera...pakurathu evalo periya santhosama iruku.....
0 (Positive)
En thalaivan vijaysethubathi kaga matume na padam papan matha entha p..... illa
2 (Mixed_feelings)
Kanithan + mugamoodi Neenga nambalainalum ithan nesam
0 (Positive)
Enakku iru mugan trailer gnabagam than varuthu
0 (Positive)
ARE YOU EXPECTING HAPPY DEEPAVALI FORCE DIALOGUE IN 2.0 MOVIE
0 (Positive)
Sakthimaan fans like Sivakarthikeyen fans comment
2 (Mixed_feelings)
rajini and akshay fans hit like
2 (Mixed_feelings)
1 hour 1 million likes
0 (Positive)
Raww vana padama iruku all the best
0 (Positive)
Thalaiva.. full Support from kerala Mohanlal Fans
3 (unknown_state)
En da.. trailer layae ivlo slow motion nah.. padathula paathi duration slow motion layae mudichirpeenga polayae
1 (Negative)
Thalaiva ungala screen pakkum pothey ippadiiii erukinga na Nerla partha yappidiiii erupinga Oru murai tharisanam kedaikumaaaa
0 (Positive)
Rajini ah vida akshay mass ah irukane
1 (Negative)
Thala koda compare panna yarukum thakuthi kidaiyathu mind it
0 (Positive)
Marana mass marana style marna gethu mass trailer rajinified
0 (Positive)
Paruthiveeran Karthi Back Lokesh Kangaraj Screenplay
3 (unknown_state)
Diwali annante aniyan ullaltha Step back bigil
2 (Mixed_feelings)
Dialogue pundaiye puriyala. Otha theatre la nacham sound seri pannungada.
1 (Negative)
Poda mokka moonjI unna pathale siriputhan varuthu super herovaam adutha mugamoodiii
1 (Negative)
dei kelthu kuthi ajith ivanya yaru nadika sona🤢🤢🤢
1 (Negative)
Yaaru ivanaa... INDHA Kosu Asoorana dei ennada Unga Rasana....
1 (Negative)
Namma kitte kaadu iruntha,eduthukuvanga Namma kitte roopa iruntha, pudukiku vaanga Padippu mattum namma kitte irundhu eduthukave mudiyathu Superb lines
0 (Positive)
Ethukkuda ivlo actors hypo....thalaivar padamna avar matumtha irukanum...
0 (Positive)
Thlaiavaa... Thalaivaa... Thaliava... Thalaivaa.... Thalaivaa...
0 (Positive)
epdi like pottavan Eallarum seathupogada
2 (Mixed_feelings)
சுயமாக சிந்திக்க தெரிஞ்சவன் தான் சூப்பர் ஹீரோ' - 100% true in the age information abundance and mass distractions.
0 (Positive)
Supper dhanush Anna mass waiting asuran
0 (Positive)
Vera lvl thalaiva u r great
0 (Positive)
Mobile main mera phek dunga iPhone x BARNA aisa problem hoga
1 (Negative)
Sala kuch smjh nhi aaya pr mja aaya
4 (not-Tamil)
Ellam avan sayal movie mathiriya irukku🤔
2 (Mixed_feelings)
Boss you looking so handsome ND young kekaa
0 (Positive)
Pattaya kelapitey Siva!!! Awesome!!!
2 (Mixed_feelings)
Dahaaaaaaa chitii come back I am waiting get ready to 2.0
2 (Mixed_feelings)
Boni kapoor sir ini entha update illama ethum release panathinga sir, intha chinna pasanga tholla thanga mudiyala
2 (Mixed_feelings)
Looks thrilling, thala get up super as lawyer , dai pandey neeya da
0 (Positive)
Neenga enna vena kathukonga aana Hollywood padathukku dubbing mattum kathukathinga
1 (Negative)
Ena da nadakuthu inga.. Epaaa sathiyamaa edir pakatha trailer.. TOTALLY UNIMAGINABLE TRAILER. THALIVAR
0 (Positive)
1:43 Thala da By thalapathy fan
0 (Positive)
Sun picture best or Vijay TV best sp best na dislike pannuga Vijay TV best na like pannuga
0 (Positive)
Hey Makkalay yaaru Darbar Motion Poster announcementku apram vantheenga
3 (unknown_state)
Last dialoge thala fans ungalukkuta Iam Vijay fan trailer super
0 (Positive)
Hollywood range la iruku padam Kandipa blockbuster hit! Semma story....
2 (Mixed_feelings)
But ore oru varutham ennana Sunday release pannirukkalam yesterday aavathu announcement pannirukkalam
0 (Positive)
Semmma style... rajini sir is back...
0 (Positive)
My Dear Thala ahhhhhhhhhhhhhh semma GETHU
0 (Positive)
Idduke ₹500 va tarlam... kolla mass
2 (Mixed_feelings)
Movie savadi vera level poo..thalaivar thalaivar thaa..mass
0 (Positive)
Super thalaiva .Semma mass. Intha movie blockbuster thaan. FDFS poravanga like pannunga.
0 (Positive)
92k views 132k likes Fraud
2 (Mixed_feelings)
3 minute trailor vida thil irukkaa....bollywood la panranga....tamila mudiyuma???????
0 (Positive)
Dai ranjit nee idha parthu film eduka kathuko
0 (Positive)
Trailer fast ah podu Ana Background la main bgm illaye Fyt Ku Varadhinga Da Unmaiya tha slura
0 (Positive)
Superb Vivek Mervin pinnitinga ponga... Ungalukagave padam pakanum
0 (Positive)
Viswasam likes aa pathu payainthu vainthavainga yar yar like pothathu.....
0 (Positive)
1:36 *thaa enna look-u daa saami That bgm lit
0 (Positive)
Guyzz 1m likes vara varaikum vitraadheenga...
0 (Positive)
Padaiyatchi nu sonna mattum ungalukku jaathi veri nagi reddi settiyar mudaliar nu sonna adhu jaathi veri illaya
3 (unknown_state)
Velraj sir thalaivana masss a kamchirukaru vera level u
0 (Positive)
U1 kaga paakuradha illa sk kaaga verukuradhane therila but mithran + u1 kaaga kandipa paakanum
2 (Mixed_feelings)
Iam vijay fan kerala and iam waiting super star's petta
0 (Positive)
Oh my god .. it's rajini for you
2 (Mixed_feelings)
0:52 thala looka apdiyae vechurkaru pa. Without audio la paata thalaikum ivrukkum vityasam teriyathu
0 (Positive)
Dai yaaru da antha 36k? apadi enna da enga thala mela ungaluku Gaandu?!
0 (Positive)
Love u thalaivaaaaaaa avangala aarani Anastasia's Alabama and Anastasia's
2 (Mixed_feelings)
Iam sk fan aanaa trailer semma
0 (Positive)
Wow padam tharu mara iruka poguthu This pongal petta diwali
0 (Positive)
Sema mass marana mass tha i love rajini
0 (Positive)
800k likes varavareku nanu thungamatte nenaikiravage like podunge
0 (Positive)
Damn..im a proud. Chiyaan vikram fan..
0 (Positive)
Enna panna porom sweet sapda porom....
0 (Positive)
entha kannada naiyai ennum tamilnattil erunthu anupalaiya?
1 (Negative)
Vaa thalaiva via diwali mass ur movie bikil out
0 (Positive)
Veralevel performer Dhanush sir
0 (Positive)
No means no entha dialogue thalaya thavara yaru sonna nallairukumnu tharayala
0 (Positive)
Super akshay sir i love u sir
0 (Positive)
Looks like he will SET SCREEN ON FIRE
0 (Positive)
Ninga vena yenkuda classah passah suthla aana yen massu yennanu teriyathulla
3 (unknown_state)
It's pink with ajith Kumar ka chutiyapa
0 (Positive)
Rmba sandhosama irukku. Thala ipdi suspense movie la nadichadhu.and rlz kaga waiting.
0 (Positive)
Villaina mass aa kattallayea. So sad. Villain mass aa iruntha thaa movie mass aa irukkum
0 (Positive)
Trailer vera level athana Da....aamai kunjugala
0 (Positive)
Yenda, padathoda ottumoththa kadhaiyayum idhudhan nu potu kaatra mariya da trailer pannuveenga.
2 (Mixed_feelings)
Nambakita kadu iruntha vangikuvanunga kasu iruntha puduingikuvanunga padipa Mattum pudungavey mudiyathu chidambaram
0 (Positive)
End of preview (truncated to 100 rows)

Dataset Card for Tamilmixsentiment

Dataset Summary

The first gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. This makes the largest general domain sentiment dataset for this relatively low-resource language with code-mixing phenomenon. The comment/post may contain more than one sentence but the average sentence length of the corpora is 1. Each comment/post is annotated with sentiment polarity at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios.

Supported Tasks and Leaderboards

To identify sentiment polarity of the code-mixed dataset of comments/posts in Tamil-English collected from social media.

Languages

Tamil-English code-switched. The dataset contains all the three types of code-mixed sentences - Inter-Sentential switch, Intra-Sentential switch and Tag switching. Most comments were written in Roman script with either Tamil grammar with English lexicon or English grammar with Tamil lexicon. Some comments were written in Tamil script with English expressions in between.

Dataset Structure

Data Instances

An example from the Tamilmixsentiment train set looks as follows:

text                                        label
Trailer late ah parthavanga like podunga    Positive 

Data Fields

  • text: Tamil-English code-mixed comment.
  • label: list of the possible sentiments "Positive", "Negative", "Mixed_feelings", "unknown_state", "not-Tamil"

Data Splits

The entire dataset of 15,744 sentences was randomly shuffled and split into three parts as follows:

train validation test
Tamilmixsentiment 11335 1260 3149

Dataset Creation

Curation Rationale

Sentiment analysis has become important in social media research (Yang and Eisenstein, 2017). Until recently these applications were created for high-resourced languages which analysed monolingual utterances. But social media in multilingual communities contains more code-mixed text. Code-mixing is common among speakers in a bilingual speech community. As English is seen as the language of prestige and education, the influence of lexicon, connectives and phrases from English language is common in spoken Tamil. Tamil has little annotated data for code-mixed scenarios. An annotated corpus developed for monolingual data cannot deal with code-mixed usage and therefore it fails to yield good results due to mixture of languages at different levels of linguistic analysis. Therefore this dataset of code-mixed Tamil-English sentiment annotated corpus is created.

Source Data

Initial Data Collection and Normalization

The data was scraped from Youtube. In total 184,573 sentences for Tamil from YouTube comments from the trailers of a movies released in 2019. Many of the them contained sentences that were either entirely written in English or code-mixed Tamil-English or fully written in Tamil. So we filtered out a non-code-mixed corpus based on language identification at comment level using the langdetect library. The comment is written fully in Tamil or English, we discarded that comment since monolingual resources are available for these languages. We also identified if the sentences were written in other languages such as Hindi, Malayalam, Urdu, Telugu, and Kannada. We preprocessed the comments by removing the emoticons and applying a sentence length filter. We want to create a code-mixed corpus of reasonable size with sentences that have fairly defined sentiments which will be useful for future research. Thus our filter removed sentences with less than five words and more than 15 words after cleaning the data. In the end we got 15,744 Tanglish sentences.

Who are the source language producers?

Youtube users

Annotations

Annotation process

Three steps complete the annotation setup. First, each sentence was annotated by two people. In the second step, the data were collected if both of them agreed. In the case of conflict, a third person annotated the sentence. In the third step, if all the three of them did not agree, then two more annotators annotated the sentences.

Who are the annotators?

Eleven volunteers were involved in the process. All of them were native speakers of Tamil with diversity in gender, educational level and medium of instruction in their school education.

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@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",
}

Contributions

Thanks to @jamespaultg for adding this dataset.

Update on GitHub

Models trained or fine-tuned on tamilmixsentiment