Özet:

Text classification is a natural language processing area for assigning text documents to one of the predetermined class labels. Text classification is used in many natural language processing problems, including sentiment analysis, topic tagging, question answering, and dialogue action classification. It has many applications, such as text classification, filtering and organization of news texts, and filtering of spam e-mail contents. In recent years, deep neural network-based architectures and neural language models have been used frequently in the field of text classification. Long-term memory-based architectures (LSTM) use gate mechanisms to reduce the vanishing and exploding gradients observed in the conventional recurrent neural networks when learning long-term dependencies. Therefore, LSTM and LSTM-based architectures have widespread use in many sequence modeling tasks. In LSTM-based architectures, although the memory cell contains the basic information, it is not allowed to directly affect the gate mechanism. In this study, the performance of recurrent neural network, long short-term memory, gated recurrent unit, peephole-based long-short-term memory architecture and working-memory connections-based long-term memory architecture are comparatively evaluated for Turkish sentiment analysis. In the representation of the corpus, word2vec, fastText and GloVe word embedding methods were evaluated. Experimental analyzes show that working memory connections-based long short-term memory architecture achieves higher classification accuracy for sentiment analysis on Turkish text documents compared to peephole-based long short-term memory architecture, long short-term memory and gated recurrent unit architecture.

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