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Nowadays, the Internet is a structure that people can access easily and at the same time produce content easily and without control. In parallel with this situation, the ability of extract information from the raw data that makes up big data has become more complex. The fact that the headlines of the contents contain uncontrolled and misleading elements makes it difficult to reach the right information. The headlines of the contents are important for people to reach the information they want in their limited time. In this study, it is aimed to produce headlines suitable for the content instead of headlines that may be misleading for news. For this purpose, an application that produces headlines for Turkish news with deep learning method has been developed. SuDer news corpus is used as dataset. For the training of the model, it is aimed to obtain more humanoid results in the production of news headlines by using the Transformer architecture, which is frequently preferred in natural language studies today and the abstract summarization method. In this study, in order to compare the performance of the Transformer model, models are prepared and trained with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. At the end of 25 epochs of training with LSTM, GRU and Transformer architectures on the corpus, the values of loss are 1.03, 0.55 and 2.49 respectively. In the experiments performed on the validation data, measurements are made with ROUGE-1, ROUGE-2 and ROUGE-L metrics. As a result of the measurements, it is observed that the Transformer architecture is partially good, based on the metric values produced. In addition, when the headlines produced with these architectures are examined, it is observed that the headline obtained with the Transformer architecture produce headlines that are partially more suitable for the news content compared to other architectures. [ABSTRACT FROM AUTHOR]

The number of social media users has increased significantly as internet access and internet usage has grown all over the world. As it is the case in any other field, raw data collected from social media platforms are now being transformed into information. Millions of pieces of content are posted every day on platforms such as Twitter, Facebook, and Instagram. Moreover, the ability to extract meaningful information from such content has become an important field of research. This study reports a system for sentiment analysis, based on the data made available on Facebook, Twitter, Instagram, YouTube along with RSS data. Logistic Regression, Random Forest and deep learning algorithm such as Long Short-Term Memory (LSTM) are used to develop the classification model used in the system built as part of this study. Dataset is a new dataset that included data collected from Twitter, used was created and labeled by us. It was found that LSTM model provided the highest accuracy among the models generated using the training dataset. The final version of the model was tested on five different social media platforms and results are communicated. [ABSTRACT FROM AUTHOR]