Open AccessTwitter Sentiment Analysis Classification in the Arabic Language using Long Short-Term Memory Neural Networks
Author(s) -
Wisam Hazım Gwad Gwad,
Imad Mahmood Ismael Ismael,
Yasemin Gültepe
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.b4565.029320
Subject(s) - sentiment analysis , computer science , popularity , arabic , social media , natural language processing , usable , artificial intelligence , recurrent neural network , term (time) , artificial neural network , linguistics , world wide web , psychology , social psychology , philosophy , physics , quantum mechanics
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.