Abstract
With the increasing use of social networks and the multilingualism that characterizes the Internet in general and the social media in particular, an increasing number of recent research works on Sentiment Analysis and Opinion Mining are tackling the analysis of informal textual content, which includes language alternation, known as code-switching. To date, very little work has addressed in particular, the analysis social media of the Tunisian dialect, which is characterized both by a frequent occurring of code-switching and by a double script (Arabic and Latin) when written on the social media. Our study aims to explore and compare various classification models based on RNNs (Recurrent Neural Networks), precisely on LSTM (Long Short-Term Memory) neural networks.
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Notes
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TSAC Corpus is available at: https://github.com/fbougares/TSAC.
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Jerbi, M.A., Achour, H., Souissi, E. (2019). Sentiment Analysis of Code-Switched Tunisian Dialect: Exploring RNN-Based Techniques. In: Smaïli, K. (eds) Arabic Language Processing: From Theory to Practice. ICALP 2019. Communications in Computer and Information Science, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-32959-4_9
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