Deep Learning-based Sentiment Analysis in Persian Language

Recently, there has been a growing interest in the use of deep learning techniques for tasks in natural language processing (NLP), with sentiment analysis being one of the most challenging areas, particularly in the Persian language. The vast amounts of content generated by Persian users on thousands of websites, blogs, and social networks such as Telegram, Instagram, and Twitter present a rich resource of information. Deep learning techniques have become increasingly favored for extracting insights from this extensive pool of raw data, although they face several challenges. In this study, we introduced and implemented a hybrid deep learning-based model for sentiment analysis, using customer review data from the Digikala Online Retailer website. We employed a variety of deep learning networks and regularization techniques as classifiers. Ultimately, our hybrid approach yielded an impressive performance, achieving an F1 score of 78.3 across three sentiment categories: positive, negative, and neutral.

Extended Mixture of MLP Experts by Hybrid of Conjugate Gradient Method and Modified Cuckoo Search

This paper investigates a new method for improving the learning algorithm of Mixture of Experts (ME) model using a hybrid of Modified Cuckoo Search (MCS) and Conjugate Gradient (CG) as a second order optimization technique. The CG technique is combined with Back-Propagation (BP) algorithm to yield a much more efficient learning algorithm for ME structure. In addition, the experts and gating networks in enhanced model are replaced by CG based Multi-Layer Perceptrons (MLPs) to provide faster and more accurate learning. The CG is considerably depends on initial weights of connections of Artificial Neural Network (ANN), so, a metaheuristic algorithm, the so-called Modified Cuckoo Search is applied in order to select the optimal weights. The performance of proposed method is compared with Gradient Decent Based ME (GDME) and Conjugate Gradient Based ME (CGME) in classification and regression problems. The experimental results show that hybrid MSC and CG based ME (MCS-CGME) has faster convergence and better performance in utilized benchmark data sets.