Open AccessSentiment Analysis with Deep Learning: A Bibliometric Review
Author(s) -
Nurnasran Puteh et.al
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i3.952
Subject(s) - sentiment analysis , computer science , deep learning , artificial intelligence , field (mathematics) , data science , natural language processing , subject (documents) , productivity , focus (optics) , machine learning , world wide web , physics , mathematics , pure mathematics , optics , economics , macroeconomics
Sentiment analysis is an active area of research in natural language processing field. Prior research indicates numerous techniques have been used to perform the sentiment classification tasks which include the machine learning approaches. Deep learning is a specific type of machine learning that has been successfully applied in various field such as computer vision and various NLP tasks including sentiment analysis. This paper attempts to provide a bibliometricanalysisof academic literature related to the sentiment analysis with deep learningmethods which were retrieved from Scopusuntil the third quarter of 2020. We focus on the analysis of the research productivity in this field, the distribution of subject categories, the sources and types of the publications, their geographic distributions, the most prolific and impactful authors and institutions, the most cited papers and the trends of keywords.This study can help researchers and practitionersin keeping abreast with the global research trends in thearea of sentiment analysis using deep learning approaches.