Akyol, Sinem; Alatas, Bilal

In recent years, social networks have become an important part of daily life and are becoming increasingly important. In this study, sentiment analysis or opinion mining, which is one of the most well-known social network analysis problems, is considered as an optimization problem for the first time. In the same way, for the first time, sentiment analysis is considered as a multi-objective problem. Whale Optimization Algorithm and Social Impact Theory based Optimization Algorithm, which are the current intelligent optimization algorithms, are adapted for the sentiment analysis problem. Furthermore, memory feature is integrated into Social Impact Theory based Optimization Algorithm in order to obtain effective results in this study. The obtained results from the proposed algorithms are also compared with thirty-three supervised learning algorithms within real IMDB, Polarity, and Amazon data sets. In order to evaluate the performance of the results; accuracy percentage, precision, recall, F-Measure, and MCC that are the five most commonly used measure in the literature are used. When the results are examined, it is observed that adapted metaheuristic optimization algorithms give more successful results in sentiment analysis problem.

AtıfScopus35·WoS27·OpenAlex43

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Can, Umit; Alatas, B.

The use of online social networks has made significant progress in recent years as the use of the Internet has become widespread worldwide as the technological infrastructure and the use of technological products evolve. It has become more suitable to reach online social networking sites such as Facebook, Twitter, Instagram and LinkedIn via the internet and web 3.0 technologies. Thus, people have shared their views on many different topics and their emotions with other users more widely on these platforms. This means that a huge amount of data is created on platforms where millions of people connect with each other through social networks. Nevertheless, the development of computational paradigms at high speed and complexity with technological possibilities allows analysis of valuable data by means of social network analysis methods. Our goal for this paper is to present a review of novel and popular online social network analysis problems with related applications and a reference work for researchers interested in analyzing online social network data and social network problems. Unlike other individual studies we have gathered 21 online social network problems and defined them with related studies. Thus, this study is original by presenting an important source of research by explaining the problems of online social network and the studies performed in this area.

AtıfScopus172·WoS113

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Aslan, Serpil; Kaya, Buket; Kaya, Mehmet

The link prediction problem can be used for predicting the link changes that are difficult to understand and costly in network science. So far, many different models have been proposed to predict links in the network. Most of the proposed models in the link prediction have focused on the unimodal networks. In this paper, we present a link prediction model that attempts to predict links in the large-scale bipartite networks. The proposed method consists of two stages: The first stage is the extraction of the potential link set. A limitation of the previous works that addressed the link prediction problem in bipartite networks is that only the structural properties of the projected networks that were transformed from the bipartite networks are used to extract the potential links. In this paper, in addition to the structural properties of the projected networks, we take into account the structural properties of the bipartite network. Also, we employ the strengthened projection network model instead of the classical projection network models. The second stage is the calculation of prediction score of each potential link. The classical proximity measures have several disadvantages in dynamic complex networks. When network evolution that considers valuable information between node pairs are ignored, the potential information source is not efficiently used in the link prediction. Therefore, in this stage, we propose a time-aware proximity measure based on social network evolution, instead of classical proximity measures using the current state of the network. To the best of our knowledge, this is the first effort that considers the evolution in bipartite networks. To test the performance of the proposed method, we made some experiments on two real networks that have different frame size. Experimental results show that the success of our method is promising.

AtıfScopus14·WoS5·OpenAlex9

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