Open AccessTraditional or deep learning for sentiment analysis : A review
Author(s) -
Aadil Gani Ganie,
Samad Dadvandipour
Publication year - 2022
Publication title -
multidiszciplináris tudományok
Language(s) - English
Resource type - Journals
eISSN - 2786-1465
pISSN - 2062-9737
DOI - 10.35925/j.multi.2022.1.1
Subject(s) - computer science , sentiment analysis , raw data , data science , deep learning , artificial intelligence , context (archaeology) , resource (disambiguation) , feature (linguistics) , machine learning , information retrieval , world wide web , computer network , linguistics , philosophy , programming language , paleontology , biology
Getting the context out of the text is the main objective of sentiment analysis. Today’s digital world provides us with many data raw forms: Twitter, Facebook, blogs, etc. Researchers need to convert this raw data into useful information for performing analysis. Many researchers devoted their precious time to get the text’s polarity using deep learning and conventional machine learning methods. In this paper, we reviewed both the approaches to gain insight into the work done. This paper will help the researchers to choose the best methods for classifying the text. We pick some of the best articles and critically analyze them in different parameters like dataset used, feature extraction technique, accuracy, and resource utilization.