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

被引:8
作者
Lasri I. [1 ]
Riadsolh A. [1 ]
Elbelkacemi M. [1 ]
机构
[1] Mohammed V University in Rabat, Rabat
关键词
big data; higher education; machine learning; sentiment analysis; Twitter;
D O I
10.3991/ijet.v18i05.35959
中图分类号
学科分类号
摘要
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 98%. 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 realtime 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 the decision-making process © 2023, International Journal of Emerging Technologies in Learning.All Rights Reserved.
引用
收藏
页码:42 / 61
页数:19
相关论文
共 40 条
[1]  
Htet H., Khaing S. S., Myint Y. Y., Tweets sentiment analysis for healthcare on big data processing and IoT architecture using maximum entropy classifier, Big Data Analysis and Deep Learning Applications, 744, pp. 28-38, (2019)
[2]  
M achuca C. R., Gallardo C., Toasa R. M., Twitter sentiment analysis on Coronavirus: machine learning approach, J. Phys. Conf. Ser, 1828, 1, (2021)
[3]  
R iadsolh A., Lasri I., ElBelkacemi M., Cloud-based sentiment analysis for measuring customer satisfaction in the Moroccan banking sector using Naive Bayes and Stanford NLP, J. Autom. Mob. Robot. Intell. Syst, 14, 4, pp. 64-71, (2020)
[4]  
Vidya N. A., Fanany M. I., Budi I., Twitter sentiment to analyze net brand reputation of mobile phone providers, 3th Information Systems International Conference, pp. 519-526, (2015)
[5]  
Omar M. F., Mahathir N. H., Mohd Nawi M. N., Zulhumadi F., Prototype development and pre-commercialization strategies for mobile based property analytics, International Journal of Interactive Mobile Technologies (iJIM), 13, 10, pp. 198-204, (2019)
[6]  
Paolanti M., Et al., Tourism destination management using sentiment analysis and geo-location information: a deep learning approach, Inf. Technol. Tour, 23, 2, pp. 241-264, (2021)
[7]  
Ansari M. Z., Aziz M. B., Siddiqui M. O., Mehra H., Singh K. P., Analysis of political sentiment orientations on Twitter, International Conference on Computational Intelligence and Data Science (ICCIDS 2019), 167, pp. 1821-1828, (2020)
[8]  
Ergul Aydin Z., Kamisli Ozturk Z., Erzurum Cicek Z. I., Turkish sentiment analysis for open and distance education systems, Turk. Online J. Distance Educ, pp. 124-138, (2021)
[9]  
Dina N. Z., Yunardi R. T., Firdaus A. A., Juniarta N., Measuring user satisfaction of educational service applications using text mining and multicriteria decision-making approach, International Journal of Emerging Technologies in Learning (iJET), 16, 17, pp. 76-88, (2021)
[10]  
Ulfa S., Bringula R., Kurniawan C., Fadhli M., Student feedback on online learning by using sentiment analysis: a literature review, 6th International Conference on Education and Technology (ICET), pp. 53-58, (2020)