OMLML: a helpful opinion mining method based on lexicon and machine learning in social networks
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ORIGINAL PAPER
OMLML: a helpful opinion mining method based on lexicon and machine learning in social networks Mohammadreza Keyvanpour1 · Zahra Karimi Zandian2 · Maryam Heidarypanah3 Received: 9 April 2019 / Revised: 17 December 2019 / Accepted: 24 December 2019 © Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract Identification of users’ polarities and mining their opinions in various areas, especially social networks, has become one of the popular and useful research fields. Although opinion mining and analyzing methods based on machine learning or lexicon have been useful, high training cost based on time or memory used, lack of enriched and complete lexicons, high dimensions of feature space and ambiguity in positive or negative detection of some sentences in these methods are examples of their downsides. To cope with these problems, in this paper a helpful method based on lexicon and machine learning called OMLML is proposed by using social networks. The main superiority of the proposed method compared to other methods is addressing these challenges simultaneously. According to the proposed method, the polarity of the opinions toward a target word is first determined using a method based on lexicon and textual features of words and sentences. Next, having mapped feature space into a 3-D vector, opinions are analyzed and classified based on a new machine learning method. The results of quantitative and qualitative experiments show that mapping data into a new space decreases training cost and that the performance of the proposed method than is acceptable particularly from the perspective of accuracy, F-measure and runtime. Keywords Opinion mining · Social network · Lexicon · Machine learning · Classification
1 Introduction With increasing expansion of the Internet, communities, social networks, the rise in their applications and number of users of social networks, the volume of data generated has increased (Chen and Qi 2011; Rahmani et al. 2014). Therefore, it makes relevant information extraction more challenging (Ali et al. 2015). On the other hand, people are more than willing and happy to share their lives, knowledge and experience (Lloret et al. 2012), and the huge amount of information has become an attractive resource for organizations to monitor the opinions of users (Zainuddin et al. 2018), and social networks have been an appropriate framework for expressing users’ opinions and ideas in various * Mohammadreza Keyvanpour [email protected] 1
Department of Computer Engineering, Alzahra University, Tehran, Iran
2
Data Mining Lab, Department of Computer Engineering, Alzahra University, Tehran, Iran
3
Department of Computer Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran
applied fields (Lee et al. 2012) and a rich resource for users’ opinions mining and sentiment analysis. Hence, mining this kind of data helps extract practical patterns which are useful for business, applications and consumers. Opinion mining is a research field that deals with in
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