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

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.

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