Sentiment Analysis of Persian-English Code-mixed Texts

TL;DR

This paper introduces a new dataset of Persian-English code-mixed tweets and a BERT-based model for sentiment analysis, outperforming traditional baseline methods in accuracy.

Contribution

The study creates the first Persian-English code-mixed sentiment dataset and proposes a BERT-based model that effectively captures sentiment in multilingual social media texts.

Findings

The BERT-based model outperforms Naive Bayes and Random Forest baselines.

Created the first annotated dataset of Persian-English code-mixed tweets.

Demonstrated the effectiveness of translation and pretrained embeddings in sentiment analysis.

Abstract

The rapid production of data on the internet and the need to understand how users are feeling from a business and research perspective has prompted the creation of numerous automatic monolingual sentiment detection systems. More recently however, due to the unstructured nature of data on social media, we are observing more instances of multilingual and code-mixed texts. This development in content type has created a new demand for code-mixed sentiment analysis systems. In this study we collect, label and thus create a dataset of Persian-English code-mixed tweets. We then proceed to introduce a model which uses BERT pretrained embeddings as well as translation models to automatically learn the polarity scores of these Tweets. Our model outperforms the baseline models that use Na\"ive Bayes and Random Forest methods.