NUIG-Shubhanker@Dravidian-CodeMix-FIRE2020: Sentiment Analysis of Code-Mixed Dravidian text using XLNet

Abstract

Social media has penetrated into multi-lingual societies, however most of them use English to be a preferred language for communication. So it looks natural for them to mix their cultural language with English during conversations resulting in abundance of multilingual data -call this code-mixed data, available in today's world. Downstream NLP tasks using such data is challenging due to the semantic nature of it being spread across multiple languages. One such NLP task is Sentiment analysis; for this we use an auto-regressive XLNet model to perform sentiment analysis on code-mixed Tamil-English and Malayalam-English datasets.

Transformer based model for offensive content recognition in dravidian languages

“…This supports the ability to learn sentiment value information about important morphemes. Effective learning on the input data to generate representation [22] helps in better performance of the required task. The proposed method learns the code-mixed input using a model that can understand multiple languages and then subjects the representation to a certain model for classification.…”