@article {ElHaddaoui2021336,
	title = {Sentiment analysis: A review and framework foundations},
	journal = {International Journal of Data Analysis Techniques and Strategies},
	volume = {13},
	number = {4},
	year = {2021},
	note = {cited By 0},
	pages = {336-355},
	abstract = {The rise of social media as a platform for opinion expression and social interactions motivated the need for an automated data analysis technique for business value extraction with optimal investment considerations. In this respect, sentiment analysis (SA) becomes the de facto approach to investigate generated data and retrieve information such as sentiments and emotions, discussed topics, etc., via traditional machine learning and modern neural network-based algorithms. The current techniques achieve reasonable accuracy scores but their performance evolution is depending on the context of application, also most implementations are complex and non-reusable components. Our literature review shows a lack in research studies to unify existing systems under a common framework for SA tasks. This paper also highlights the rending movement of neural networks approaches and pinpoint recent research studies for SA sub tasks. A SA framework design proposition is presented based on key research projects and enhanced with other promising works. {\textcopyright} 2021 Inderscience Publishers. All rights reserved.},
	doi = {10.1504/IJDATS.2021.120112},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122875189\&doi=10.1504\%2fIJDATS.2021.120112\&partnerID=40\&md5=e3d2033b898f101c2ec5d087fdf6472e},
	author = {El Haddaoui, B. and Chiheb, R. and Faizi, R. and El Afia, A.}
}
