Investigating the Effect of Segmentation Methods on Neural Model based Sentiment Analysis on Informal Short Texts in Turkish
This work addresses the challenge of sentiment analysis for informal Turkish texts, which is an incremental improvement in a domain-specific context.
The study examined how different text segmentation methods affect the performance of neural models for sentiment analysis on informal Turkish short texts, finding that hybrid segmentation approaches generally yielded the best results, with specific methods achieving up to 87.5% accuracy.
This work investigates segmentation approaches for sentiment analysis on informal short texts in Turkish. The two building blocks of the proposed work are segmentation and deep neural network model. Segmentation focuses on preprocessing of text with different methods. These methods are grouped in four: morphological, sub-word, tokenization, and hybrid approaches. We analyzed several variants for each of these four methods. The second stage focuses on evaluation of the neural model for sentiment analysis. The performance of each segmentation method is evaluated under Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model proposed in the literature for sentiment classification.