A Novel Lexicon-Based Approach for Sentiment Analysis in Turkish

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This study investigates a target-based sentiment analysis approach on Turkish texts and examines how lexicon-based methods vary depending on language compatibility and translation strategies. The main objective is to accurately identify target-oriented expressions and to compare the performance of different sentiment lexicons within this context. For this purpose, Turkish user reviews obtained from the Turkish school review and evaluation platform were analysed using three lexicon configurations: SentiWordNet applied in its original English form with target-related term translation (SentiWordNet-EN), its fully Turkish-translated version (SentiWordNet-TR), and a native Turkish resource (SentiTurkNet). SentiTurkNet achieved the highest weighted average F1-score of 0.887 (positive-class F1: 0.926; negative-class F1: 0.760), followed by SentiWordNet-EN with a weighted average F1-score of 0.856 (positive-class F1: 0.898; negative-class F1: 0.720), and SentiWordNet-TR with a weighted average F1-score of 0.824 (positive-class F1: 0.868; negative-class F1: 0.679). One of the most significant findings is that using SentiWordNet in its original English form yields better results than the fully translated version, suggesting that the translation process leads to sentiment loss due to the incomplete preservation of sentiment intensity and contextual meaning. These findings carry important implications for sentiment analysis in low-resource languages: where comprehensive native lexicons are unavailable, translating only target-related terms into a language with richer sentiment resources can be more effective than directly translating the entire lexicon. Therefore, it is concluded that in target-based sentiment analysis, not only language compatibility but also the chosen translation strategy plays a critical role.