Sentiment Analysis with Deep Learning: A Bibliometric Review

Nurnasran Puteh Et.Al

doi:10.17762/turcomat.v12i3.952

Abstract

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.

Highlights

Sentiment analysis (SA) is a field of study in Natural Language Processing (NLP)
We explored the trend of global research in the area of SA with deep learning approaches by performing a bibliometric analysis of the 681 publications obtained from the Scopus database which were published until near the third quarter of the year 2020
The results show that publications in this area started at 2011 and begun to rise incrementally, with an average annual growth rate of 12%, from 2013 until 2020

Summary

Introduction

Sentiment analysis (SA) is a field of study in Natural Language Processing (NLP) It is defined as the task of classifying people’s sentiments or opinions towards certain entities ranging from products, services, organizations to events and current issues (Liu, 2015). With the advent of Web 2.0 technology, there is an increased number of people expressing their opinions in the social media such as Facebook, Twitter, blogs and forums. This has resulted in huge amount of unstructured data that need to be analyzed so that the people’s sentiments can be identified (Pang & Lee, 2008; Singh et al, 2016). These supervised machine learning approaches include traditional machine learning methods such as Support Vector Machines (Alves et al, 2014), Maximum Entropy (Wu, Li& Xie, 2017) and Naïve Bayes (Parveen & Pandey, 2017)

Methods

Results

Conclusion