Twitter Sentiment Analysis Classification in the Arabic Language using Long Short-Term Memory Neural Networks
Wisam Hazım Gwad Gwad1, Imad Mahmood Ismael Ismael2, Yasemin Gültepe3
1Wisam Hazım Gwad Gwad, Institute of Science and Technology, Kastamonu University, Kastamonu, Turkey.
2Imad Mahmoos Ismael Ismael, Institute of Science and Technology, Kastamonu University, Kastamonu, Turkey. Yasemin Gültepe*, Department of Computer Engineering, Kastamonu University, Kastamonu, Turkey.
Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 235-239 | Volume-9 Issue-3, February, 2020. | Retrieval Number: B4565129219/2020©BEIESP | DOI: 10.35940/ijeat.B4565.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The increasing use of social media and the idea of extracting meaningful expressions from renewable and usable data which is one of the basic principles of data mining has increased the popularity of Sentiment Analysis which is an important working area recently and has expanded its usage areas. Compiled messages shared from social media can be meaningfully labeled with sentiment analysis technique. Sentiment analysis objectively indicates whether the expression in a text is positive, neutral, or negative. Detecting Arabic tweets will help for politicians in estimating universal incident-based popular reports and people’s comments. In this paper, classification was conducted on sentiments twitted in the Arabic language. The fact that Arabic has twisted language features enabled it to have a morphologically rich structure. In this paper we have used the Long Short Term Memory (LSTM), a widely used type of the Recurrent Neural Networks (RNNs), to analyze Arabic twitter user comments. Compared to conventional pattern recognition techniques, LSTM has more effective results in terms of having less parameter calculation, shorter working time and higher accuracy.
Keywords: Semantic analysis, Arabic language, classification, deep learning.