Sentiment analysis with recurrent neural networks on turkish reviews domain

Rysbek, Darkhan
Easier access to computers, mobile devices, and availability of the Internet have given people the opportunity to use social media more frequently and with more convenience. Social media comes in many forms, including blogs, forums, business networks, review sites, and social networks. Therefore, social media generates massive sources of information in the shape of users‘ views, opinions, and arguments about various products, services, social events, and politics. By well-structuring and analysing this kind of data we can obtain significant feedbacks about products and services. This area of research is typically called sentiment analysis or opinion mining. In the last decade, this field of Natural Language Processing (NLP) has witnessed a fascinating progress due to Deep Neural Networks (DNNs). Recurrent Neural Networks (RNNs) are one of the main types of DNN architectures which are used at modelling units in sequence. They have been successfully used for sequence labelling and sequence prediction tasks, such as handwriting recognition, language modelling, machine translation, and sentiment analysis. Most of the studies carried on sentiment analysis using RNNs have been focused on English texts and some researches have studied on different languages. In this thesis, sentiment classification using RNNs is applied on Turkish reviews domain. Additionally, different types of word representations are used to achieve acceptable results. This dissertation presents a description of the considered model architectures and comparison of them with various word representations on two Turkish movie reviews datasets. Generally, our experimental results show that RNN models achieve reasonably good results on Turkish texts as on English texts and choice of different word representations can improve the performance of the approaches.


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Citation Formats
D. Rysbek, “Sentiment analysis with recurrent neural networks on turkish reviews domain,” Thesis (M.S.) -- Graduate School of Applied Mathematics. Scientific Computing., Middle East Technical University, 2019.