An Overview of Sentiment Analysis: Levels, Approaches and Challenges

Loukmane Maada, Khalid Al Fararni, Badreddine Aghoutane, Yousef Farhaoui, Mohammed Fattah

2021

Abstract

Text is a huge data source; it contains opinions, facts, feelings ... basically embedded knowledge. Sentiment Analysis (S.A) primary goal is to analyse the text and determine its polarity (positive, negative, neutral). This field of research has been on the rise since the beginning of this century. A lot of approaches, from word preprocessing and embedding techniques to complex big data architecture., have been modelled, tested and proposed. This paper provides an overview of the different sentiment analysis approaches, namely the traditional machine learning approach, the deep learning approach, the lexicon-based approach, and the hybrid approach. In addition, a brief insight into the challenges S.A faces and some proposed solutions are displayed.

Paper Citation

in Harvard Style

Maada L., Al Fararni K., Aghoutane B., Farhaoui Y. and Fattah M. (2021). An Overview of Sentiment Analysis: Levels, Approaches and Challenges. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 413-418. DOI: 10.5220/0010735700003101

in Bibtex Style

@conference{bml21,
author={Loukmane Maada and Khalid Al Fararni and Badreddine Aghoutane and Yousef Farhaoui and Mohammed Fattah},
title={An Overview of Sentiment Analysis: Levels, Approaches and Challenges},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={413-418},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010735700003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - An Overview of Sentiment Analysis: Levels, Approaches and Challenges
SN - 978-989-758-559-3
AU - Maada L.
AU - Al Fararni K.
AU - Aghoutane B.
AU - Farhaoui Y.
AU - Fattah M.
PY - 2021
SP - 413
EP - 418
DO - 10.5220/0010735700003101