Twitter Dataset and Evaluation of Transformers for Turkish Sentiment Analysis
Abdullatif Köksal, Arzucan Özgür
Abstract
Sentiment analysis is one of the key topics in Natural Language Processing which helps several applications from social media analysis to stock market prediction. Sentiment analysis datasets are generally collected by semi-supervision through shopping or review websites. These datasets are constructed by mapping users' text reviews to the given scores by users. However, these datasets might contain errors due to automatic mapping, and generally they don't have the characteristic features of social media texts such as emojis, slangs, and typos. To address these problems, one of the first manually annotated Turkish Sentiment Analysis datasets from Twitter is proposed. The BounTi dataset contains Turkish tweets about specific universities at Turkey. Furthermore, the performance of multilingual and Turkish transformer models such as MBERT, XLM-Roberta, and BERTurk are analyzed for this dataset. The best proposed model https://github.com/boun-tabi/BounTi-Turkish-Sentiment-Analysis is based on BERTurk and achieves 0.729 macro-averaged recall score on the test set. Finally, a social media analysis demonstration with the best model is performed on Turkish tweets about a food brand. BounTi dataset, finetuned models, and related scripts are publicly released.