Sentiment Analysis of Persian-English Code-mixed Texts
This addresses the need for sentiment analysis in code-mixed social media texts, particularly for Persian-English contexts, but is incremental as it builds on existing multilingual methods.
The study tackled sentiment analysis of Persian-English code-mixed tweets by creating a labeled dataset and introducing a model using BERT embeddings and translation models, which outperformed baseline models like Naïve Bayes and Random Forest.
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 Naïve Bayes and Random Forest methods.