Developing a Comprehensive Sentiment Analysis Framework for Turkish

Sentiment analysis is the process of extracting the sentiment (positive, negative, neutral) in texts using natural language processing and machine learning techniques. This process has five components: the sentiment, person owing the sentiment, time period of the sentiment, the object and the aspect of the object related to the sentiment. There is no sentiment analysis work for Turkish that takes these five parts into account. In this project, we will form a sentiment analysis framework for Turkish. The works conducted for foreign languages cannot be adapted to Turkish easily. Moreover, most of the sentiment analysis studies for Turkish employ supervised methods; they do not use unsupervised or semi-supervised methods. This poses problems when supervised data are limited. Also, Turkish studies usually give rise to less satisfactory results compared to other languages. Other issues are forming sentiment vectors and building an aspect-based sentiment analysis system. In this project, a framework for Turkish sentiment analysis that incorporates polarity, its score, related object, its time, and owing person. For this purpose, neural networks and unsupervised/semi-supervised methods will be used. Finally, domain-specific sentiment lexicons will be built.