Emotion analysis of Arabic tweets using deep learning approach

• Published in: Journal of big data 2019-10, Vol.6 (1), p.1-12, Article 89
• Main Authors: Baali, Massa, Ghneim, Nada
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Big Data; Big Data—emotion recognition of Arabic texts; CNN sentence classification; Communications Engineering; Computational Science and Engineering; Computer Science; Data mining; Data Mining and Knowledge Discovery; Database Management; Datasets; Deep learning; Digital media; Emotion recognition; Emotions; Information Storage and Retrieval; Machine learning; Mathematical Applications in Computer Science; Networks; Social networks; SVM; Words (language)
• ISSN: 2196-1115; 2196-1115
• DOI: 10.1186/s40537-019-0252-x

Nowadays, sharing moments on social networks have become something widespread. Sharing ideas, thoughts, and good memories to express our emotions through text without using a lot of words. Twitter, for instance, is a rich source of data that is a target for organizations for which they can use to analyze people’s opinions, sentiments and emotions. Emotion analysis normally gives a more profound overview of the feelings of an author. In Arabic Social Media analysis, nearly all projects have focused on analyzing the expressions as positive, negative or neutral. In this paper we intend to categorize the expressions on the basis of emotions, namely happiness, anger, fear, and sadness. Different approaches have been carried out in the area of automatic textual emotion recognition in the case of other languages, but only a limited number were based on deep learning. Thus, we present our approach used to classify emotions in Arabic tweets. Our model implements a deep Convolutional Neural Networks (CNN) trained on top of trained word vectors specifically on our dataset for sentence classification tasks. We compared the results of this approach with three other machine learning algorithms which are SVM, NB and MLP. The architecture of our deep learning approach is an end-to-end network with word, sentence, and document vectorization steps. The deep learning proposed approach was evaluated on the Arabic tweets dataset provided by SemiEval for the EI-oc task, and the results-compared to the traditional machine learning approaches-were excellent.