Turkish tweet sentiment analysis with word embedding and machine learning

Abstract

This work includes processing and classification of tweets which are written in Turkish language. Four different sector tweet datasets are vectorized with Word Embedding model and classified with Support Vector Machine and Random Forests classifiers and results have been compared. We have showed that sector based tweet classification is more successful compared to general tweets. Accuracy rates for Banking sector is 89.97%, for Football 84.02%, for Telecom 73.86%, for Retail 63.68% and for overall 74.60% have been achieved.