A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts
This work addresses a data scarcity problem for Turkish NLP researchers, but it is incremental as it applies existing methods to a new language.
The authors tackled the lack of annotated data for targeted sentiment analysis in Turkish by creating a new dataset and proposing BERT-based models, which outperformed traditional models on this task.
Targeted Sentiment Analysis aims to extract sentiment towards a particular target from a given text. It is a field that is attracting attention due to the increasing accessibility of the Internet, which leads people to generate an enormous amount of data. Sentiment analysis, which in general requires annotated data for training, is a well-researched area for widely studied languages such as English. For low-resource languages such as Turkish, there is a lack of such annotated data. We present an annotated Turkish dataset suitable for targeted sentiment analysis. We also propose BERT-based models with different architectures to accomplish the task of targeted sentiment analysis. The results demonstrate that the proposed models outperform the traditional sentiment analysis models for the targeted sentiment analysis task.