Work

A Sentiment Analysis Dataset for Code-Mixed Malayalam-English

Year: 2020

Type: article

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.

Source: ARAN (University of Galway Research Repository) (Ollscoil na Gaillimhe – University of Galway)

DOI: 10.13025/21374

Authors: Chakravarthi, Bharathi Raja, Jose, Navya, Suryawanshi, Suryawanshi, Sherly, Elizabeth, McCrae, John Philip

Institutions: Ollscoil na Gaillimhe – University of Galway, Indian Institute of Information Technology and Management, Kerala

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green