DravidianCodeMix: sentiment analysis and offensive language identification dataset for Dravidian languages in code-mixed text.

This paper describes the development of a multilingual, manually annotated dataset for three under-resourced Dravidian languages generated from social media comments. The dataset was annotated for sentiment analysis and offensive language identification for a total of more than 60,000 YouTube commen...

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Published in:Language Resources & Evaluation Vol. 56; no. 3; pp. 765 - 807
Main Authors: Chakravarthi, Bharathi Raja, Priyadharshini, Ruba, Muralidaran, Vigneshwaran, Jose, Navya, Suryawanshi, Shardul, Sherly, Elizabeth, McCrae, John P.
Format: Article
Published: Springer Nature Sep2022
Subjects:
Online Access:View this record in EBSCOhost
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        10.1007/s10579-022-09583-7
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          Chakravarthi, Bharathi Raja
          Priyadharshini, Ruba
          Muralidaran, Vigneshwaran
          Jose, Navya
          Suryawanshi, Shardul
          Sherly, Elizabeth
          McCrae, John P.
        affil:
          Insight SFI Research Centre for Data Analytics, Data Science Institute, National University of Ireland Galway, Galway, Ireland
          ULTRA Arts and Science College, Madurai, Tamil Nadu, India
          School of Computer Science and Informatics, Cardiff University, Cardiff, UK
          Indian Institute of Information Technology and Management-Kerala, Kazhakkoottam, Kerala, India
      su:
        Google Inc.
        Sentiment analysis
        User-generated content
        Deep learning
        Machine learning
        Social media
      sug:
        subj:
          Google Inc.
          Sentiment analysis
          User-generated content
          Deep learning
          Machine learning
          Social media
      keyword:
        Code-mixed
        Corpora
        Dravidian languages
        Kannada
        Malayalam
        Offensive language identification
        Tamil
      ab: This paper describes the development of a multilingual, manually annotated dataset for three under-resourced Dravidian languages generated from social media comments. The dataset was annotated for sentiment analysis and offensive language identification for a total of more than 60,000 YouTube comments. The dataset consists of around 44,000 comments in Tamil-English, around 7000 comments in Kannada-English, and around 20,000 comments in Malayalam-English. The data was manually annotated by volunteer annotators and has a high inter-annotator agreement in Krippendorff's alpha. The dataset contains all types of code-mixing phenomena since it comprises user-generated content from a multilingual country. We also present baseline experiments to establish benchmarks on the dataset using machine learning and deep learning methods. The dataset is available on Github and Zenodo.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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