Sentiment Analysis in Turkish with Deep Learning

Demirci G. M., Keskin S. R., Dogan G.

2019 IEEE International Conference on Big Data, Big Data 2019, California, United States Of America, 9 - 12 December 2019, pp.2215-2221 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/bigdata47090.2019.9006066
  • City: California
  • Country: United States Of America
  • Page Numbers: pp.2215-2221
  • Keywords: Turkish NLP, Machine Learning, Sentiment Analysis, Binary Text Classification, Deep Learning
  • Yıldız Technical University Affiliated: Yes


Social media (such as Twitter) helps users express their opinions easily through writings. With the increase in the use of social media, the amount of textual data coming from direct users has grown enormously. These data can help to extract meanings of the specific situations using text mining. For example, in moments of disaster, social media can he used to get important information about the current situation. Understanding the emotions of the users can help humanitarian aid to be allocated more effectively. Sentiment analysis is a method used to get emotion rank from the textual data. It helps get the numerical scale of the emotion from the unstructured data. This study aims to create sentiment analysis, also known as opinion mining, from the given data by applying both machine learning and deep learning methods for disaster moments. We intend to see which technique is more successful in terms of sentiment analysis through comparison of the methods. We created a Turkish sentiment analysis about the coup attempt that happened on July 15, 2016. In this work, the coup attempt is considered a disaster as it consisted of violence and happened suddenly. The dataset used is comprised of unstructured sentences and labels with sentiment points that are either positive or negative. In this work, we pre-process the dataset and apply Turkish Natural Language Processing (NLP). Three text-features (Bag of Words, Tf-Idf, Word2Vec) are used to vectorize and prepare the dataset for the application of the models. Then, we apply machine learning and deep learning methods and compare them while looking at the results. Examining the results and comparison, we aim to see whether social media can help disaster management. In this comparison, we observed that deep learning has higher accuracy. Our findings indicated that social media can be used to understand sudden situations like disasters and may help to decide how to act.