TY - JOUR ID - 1978 TI - Convolutional Neural Network Equipped with Attention Mechanism and Transfer Learning for Enhancing Performance of Sentiment Analysis JO - Journal of AI and Data Mining JA - JADM LA - en SN - 2322-5211 AU - Sadr, H. AU - Pedram, Mir M. AU - Teshnehlab, M. AD - Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran. AD - Department of Electrical and Computer Engineering Faculty of Engineering, Kharazmi University, Tehran, Iran. AD - Industrial Control Center of Excellence, Faculty of Electrical and Computer Engineering, K. N. Toosi University, Tehran, Iran. Y1 - 2021 PY - 2021 VL - 9 IS - 2 SP - 141 EP - 151 KW - Sentiment Analysis KW - deep learning KW - Convolutional neural network KW - Attention mechanism KW - Transfer learning DO - 10.22044/jadm.2021.9618.2100 N2 - With the rapid development of textual information on the web, sentiment analysis is changing to an essential analytic tool rather than an academic endeavor and numerous studies have been carried out in recent years to address this issue. By the emergence of deep learning, deep neural networks have attracted a lot of attention and become mainstream in this field. Despite the remarkable success of deep learning models for sentiment analysis of text, they are in the early steps of development and their potential is yet to be fully explored. Convolutional neural network is one of the deep learning methods that has been surpassed for sentiment analysis but is confronted with some limitations. Firstly, convolutional neural network requires a large number of training data. Secondly, it assumes that all words in a sentence have an equal contribution to the polarity of a sentence. To fill these lacunas, a convolutional neural network equipped with the attention mechanism is proposed in this paper which not only takes advantage of the attention mechanism but also utilizes transfer learning to boost the performance of sentiment analysis. According to the empirical results, our proposed model achieved comparable or even better classification accuracy than the state-of-the-art methods. UR - https://jad.shahroodut.ac.ir/article_1978.html L1 - https://jad.shahroodut.ac.ir/article_1978_c66abbfcc75d6a335686da637c4f7254.pdf ER -