Integrating hierarchical gated attention network and recurrent neural network for improved sentiment classification of diverse Turkish texts

Sentiment classification is a significant process of determining contexts and trends in texts. Due to recent advancements in natural language processing and deep learning, methods such as neural networks have gained much more importance in sentiment classification. This study specifically highlights embedding methods, attention networks and mechanisms, and Recurrent Neural Networks (RNNs). The study investigates how embeddings of character and word improve models and the analysis of text, how attention mechanisms lay stress on and give more attention to certain parts of information, and how Long Short-Term Memory (LSTM) networks capture long-term dependencies and temporal changes in the text rather effectively. In order to analyze the sentiments of both short, unstructured and long, structured Turkish texts in a single model, a hybrid model is developed by integrating LSTM and Hierarchical Gated Attention Network. Furthermore, to evaluate the performance of the proposed model a new Turkish dataset has been created and labeled using Twitter data. Hierarchical Gated Attention Network plays a dual role by prioritizing essential information at both the word and sentence levels, thereby capturing the hierarchical structure of text more effectively. This approach, combined with LSTM's strong capabilities, creates a robust framework for sentiment classification. The experiment results show that the integration of these methods increases the performance of sentiment classification and provides a more comprehensive understanding of contexts in Turkish texts. The study examines in detail how these techniques are applied and the effects of these applications on the sentiment classification of the model and compares them to other configurations, methods and models.