Abstract
Due to the revolution of web 2.0, the amount of opinionated data has been extremely increased, produced by online users through sharing comments, videos, pictures, reviews, news and opinions. Although Twitter is one of the most prevalent social networking, the gathered data from Twitter is highly disorganized. However, extracting useful information from tweets is considered a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. There has been a lot of work on sentiment analysis in English texts. However, the datasets and the publications of Arabic tweets analysis are still somewhat limited. In addition, one of the main important issues is that users can change their opinions on different subjects over time. In this work, two main points are discussed. First, a deep neural network (DNN) approach (back propagation algorithm) is applied to Arabic tweets to two different domains: Egyptian stock exchange and sports’ tweets. Second, DNN is implemented to detect users’ attitude in a time period of two years for each dataset (2014 and 2015) and (2012 and 2013). The datasets are manually annotated via constructing a lexicon from the two already existing ones. When DNN performance is evaluated an average value of accuracy 90.22%, precision 90.56%, recall 90.90%, and F-measure of 90.68%, when compared to other three machine learning algorithms Naïve Bayes (NB), Decision Tree, and K-Nearest.
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Abdelhade, N., Soliman, T.H.A., Ibrahim, H.M. (2018). Detecting Twitter Users’ Opinions of Arabic Comments During Various Time Episodes via Deep Neural Network. In: Hassanien, A., Shaalan, K., Gaber, T., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-64861-3_22
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DOI: https://doi.org/10.1007/978-3-319-64861-3_22
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