Real-time Twitter Sentiment Analysis for Moroccan Universities using Machine Learning and Big Data Technologies

Authors

  • Imane Lasri Laboratory of Conception and Systems (Electronics, Signals and Informatics), Faculty of Sciences Rabat, Mohammed V University in Rabat, Rabat, Morocco https://orcid.org/0000-0002-1481-094X
  • Anouar Riadsolh Laboratory of Conception and Systems (Electronics, Signals and Informatics), Faculty of Sciences Rabat, Mohammed V University in Rabat, Rabat, Morocco
  • Mourad Elbelkacemi Laboratory of Conception and Systems (Electronics, Signals and Informatics), Faculty of Sciences Rabat, Mohammed V University in Rabat, Rabat, Morocco

DOI:

https://doi.org/10.3991/ijet.v18i05.35959

Keywords:

higher education, sentiment analysis, machine learning, big data

Abstract


In recent years, sentiment analysis (SA) has raised the interest of researchers in several domains, including higher education. It can be applied to measure the quality of the services supplied by the higher education institution and construct a university ranking mechanism from social media like Twitter. Hence, this study presents a novel system for Twitter sentiment prediction on Moroccan public universities in real-time. It consists of two phases: offline sentiment analysis phase and real-time prediction phase. In the offline phase, the collected French tweets about twelve Moroccan universities were classified according to their sentiment into ‘positive’, ‘negative’, or ‘neutral’ using six machine learning algorithms (random forest, multinomial Naive Bayes classifier, logistic regression, decision tree, linear support vector classifier, and extreme gradient boosting) with the term frequency-inverse document frequency (TF-IDF) and count vectorizer feature extraction techniques. The results reveal that random forest classifier coupled with TF-IDF has obtained the best test accuracy of 90%. This model was then applied on real-time tweets. The real-time prediction pipeline comprises Twitter streaming API for data collection, Apache Kafka for data ingestion, Apache Spark for real-time sentiment analysis, Elasticsearch for real-time data exploration, and Kibana for data visualization. The obtained results can be used by the Ministry of higher education, scientific research and innovation of Morocco for decision-making process.

Author Biographies

Imane Lasri, Laboratory of Conception and Systems (Electronics, Signals and Informatics), Faculty of Sciences Rabat, Mohammed V University in Rabat, Rabat, Morocco

Imane Lasri is a PhD student at the Faculty of Sciences Rabat, University Mohammed V in Rabat, Morocco. She received a Master’s degree in Big Data Engineering from the Faculty of Sciences Rabat. She received the awards of excellence of the major winners from the Mohammed V University in Rabat in 2019. Her current field of research is pattern recognition applied to higher education using deep learning algorithms. She is interested in artificial neural networks and deep learning. She is the author of many
research studies published in international journals and conference proceedings.

Anouar Riadsolh, Laboratory of Conception and Systems (Electronics, Signals and Informatics), Faculty of Sciences Rabat, Mohammed V University in Rabat, Rabat, Morocco

Anouar Riadsolh received his PhD in Computer Science from the Faculty of Sciences Rabat (FSR), University Mohammed V in Rabat, Morocco. He is a Professor at the FSR. He is a member of the laboratory of conception and systems (electronics, signals, and Informatics), FSR. His current research interests are focused on data mining, big data, and machine learning.

Mourad Elbelkacemi, Laboratory of Conception and Systems (Electronics, Signals and Informatics), Faculty of Sciences Rabat, Mohammed V University in Rabat, Rabat, Morocco

Mourad Elbelkacemi receives his PhD in Computer Science. He was the dean of the Faculty of Sciences Rabat (FSR). He is a Professor at the FSR. He is a member of the laboratory of conception and systems (electronics, signals, and Informatics), FSR, University Mohammed V in Rabat, Morocco. His main research interests are focused on electronics, education, data mining, and big data.

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Published

2023-03-07

How to Cite

Lasri, I., Riadsolh, A., & Elbelkacemi, M. (2023). Real-time Twitter Sentiment Analysis for Moroccan Universities using Machine Learning and Big Data Technologies. International Journal of Emerging Technologies in Learning (iJET), 18(05), pp. 42–61. https://doi.org/10.3991/ijet.v18i05.35959

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Papers