The Comparison of Language Models with a Novel Text Filtering Approach for Turkish Sentiment Analysis

Abstract

Today, comments can be made on many topics on web platforms with the development of the internet. Analyzing the data of these comments is essential for companies and data scientists. There are many methods for analyzing data. Recently, language models have also been used in many studies for sentiment analysis or text classification. In this study, Turkish sentiment analysis is performed using language models on hotel and movie review datasets. The language models are chosen because they are rarely used in Turkish literature. The pre-trained BERT, ALBERT, ELECTRA, and DistilBERT models for the Turkish language are trained and tested with these datasets. In addition, a text filtering method, which removes the words that can provide the opposition sentiment in the positive or negative labeled text, is proposed for sentiment analysis. These datasets obtained by this method are also retrained with language models and the accuracy values of their models are measured. The results of this study are compared with previous studies using the same datasets. As a result of the analysis, the accuracy values obtain state-of-the-art results with language models compared to previous studies. The best performance has been achieved by training the ELECTRA language model using the proposed text filtering method.