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Targeted Sentiment Analysis aims to extract sentiment towards a particulartarget from a given text. It is a field that is attracting attention due to theincreasing accessibility of the Internet, which leads people to generate anenormous amount of data. Sentiment analysis, which in general requiresannotated data for training, is a well-researched area for widely studiedlanguages such as English. For low-resource languages such as Turkish, there isa lack of such annotated data. We present an annotated Turkish dataset suitablefor targeted sentiment analysis. We also propose BERT-based models withdifferent architectures to accomplish the task of targeted sentiment analysis.The results demonstrate that the proposed models outperform the traditionalsentiment analysis models for the targeted sentiment analysis task.

A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts