Turkish Journal of Electrical Engineering and Computer Sciences
Sentiment analysis attempts to resolve the senses or emotions that a writer or speaker intends to send across to the people about an object or event. It generally uses natural language processing and/or artificial intelligence techniques for processing electronic documents and mining the opinion specified in the content. In recent years, researchers have conducted many successful sentiment analysis studies for the English language which consider many words and word groups that set emotion polarities arising from the English grammar structure, and then use datasets to test their performance. However, there are only a limited number of studies for the Turkish language, and these studies have lower performance results compared to those studies for English. The reasons for this can be incorrect translation of datasets from English into Turkish and ignoring the special grammar structures in the latter. In this study, special Turkish words and linguistic constructs which affect the polarity of a sentence are determined with the aid of a Turkish linguist, and an appropriate lexicon-based polarity determination and calculation approach is introduced for this language. The proposed methodology is tested using different datasets collected from Twitter, and the test results show that the proposed system achieves better accuracy than the previously developed lexical-based sentiment analysis systems for Turkish. The authors conclude that especially analysis of word groups increases the overall performance of the system significantly.
Sentiment analysis, lexicon-based, Turkish language, opinion mining
YURTALAN, GÖKHAN; KOYUNCU, MURAT; and TURHAN, ÇİĞDEM
"A polarity calculation approach for lexicon-based Turkish sentiment analysis,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 27:
2, Article 47.
Available at: https://journals.tubitak.gov.tr/elektrik/vol27/iss2/47
Computer Engineering Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons