Learning Word Representations for Tunisian Sentiment Analysis
This addresses sentiment analysis for Tunisian dialect users on social media, an incremental contribution due to scarce resources in this domain.
The paper tackled sentiment analysis for the Tunisian dialect (TUNIZI) on social media by exploring unsupervised word representations like word2vec and BERT with CNNs and BiLSTMs, achieving comparable performance to other languages on two datasets without handcrafted features.
Tunisians on social media tend to express themselves in their local dialect using Latin script (TUNIZI). This raises an additional challenge to the process of exploring and recognizing online opinions. To date, very little work has addressed TUNIZI sentiment analysis due to scarce resources for training an automated system. In this paper, we focus on the Tunisian dialect sentiment analysis used on social media. Most of the previous work used machine learning techniques combined with handcrafted features. More recently, Deep Neural Networks were widely used for this task, especially for the English language. In this paper, we explore the importance of various unsupervised word representations (word2vec, BERT) and we investigate the use of Convolutional Neural Networks and Bidirectional Long Short-Term Memory. Without using any kind of handcrafted features, our experimental results on two publicly available datasets showed comparable performances to other languages.