ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text

TL;DR

This paper introduces GenMA, a deep neural network model that automatically infers language morphology to analyze sentiment in English-Hindi code-mixed tweets, outperforming baseline models in SemEval 2020.

Contribution

The novel GenMA model automatically infers language morphology without word-level tags, improving sentiment analysis accuracy in code-mixed social media text.

Findings

Outperformed baseline F1-score on test data

Successfully inferred language morphology without explicit tags

Effective in sentiment prediction for code-mixed tweets

Abstract

Code mixing is a common phenomena in multilingual societies where people switch from one language to another for various reasons. Recent advances in public communication over different social media sites have led to an increase in the frequency of code-mixed usage in written language. In this paper, we present the Generative Morphemes with Attention (GenMA) Model sentiment analysis system contributed to SemEval 2020 Task 9 SentiMix. The system aims to predict the sentiments of the given English-Hindi code-mixed tweets without using word-level language tags instead inferring this automatically using a morphological model. The system is based on a novel deep neural network (DNN) architecture, which has outperformed the baseline F1-score on the test data-set as well as the validation data-set. Our results can be found under the user name "koustava" on the "Sentimix Hindi English" page