Numeric Polarity Detection based on Employing Recursive Deep Neural Networks and Supervised Learning on Persian Reviews of E-Commerce Users in Opinion Mining Domain
Subject Areas : electrical and computer engineeringSepideh Jamshidinejad 1 , Fatemeh Ahmadi-Abkenari 2 , Peiman Bayat 3
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2 - Department of Computer Engineering and Information Technology, Payam-Nour University, Iran
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Keywords: Deep neural networks, natural language processing, opinion mining, sentence polarity detection, sentiment Analysis, spam opinion detection,
Abstract :
Opinion mining as a sub domain of data mining is highly dependent on natural language processing filed. Due to the emerging role of e-commerce, opinion mining becomes one of the interesting fields of study in information retrieval scope. This domain focuses on various sub areas such as polarity detection, aspect elicitation and spam opinion detection. Although there is an internal dependency among these sub sets, but designing a thorough framework including all of the mentioned areas is a highly demanding and challenging task. Most of the literatures in this area have been conducted on English language and focused on one orbit with a binary outcome for polarity detection. Although the employment of supervised learning approaches is among the common utilizations in this area, but the application of deep neural networks has been concentrated with various objectives in recent years so far. Since the absence of a trustworthy and a complete framework with special focuses on each impacting sub domains is highly observed in opinion mining, hence this paper concentrates on this matter. So, through the usage of opinion mining and natural language processing approaches on Persian language, the deep neural network-based framework called RSAD that was previously suggested and developed by the authors of this paper is optimized here to include the binary and numeric polarity detection output of sentences on aspect level. Our evaluation on RSAD performance in comparison with other approaches proves its robustness.
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