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.

Detaylı bilgi için lütfen tıklayınız.