Open Access Library Journal
2020, Volume 7, e6174
ISSN Online: 2333-9721
ISSN Print: 2333-9705
DOI:
10.4236/oalib.1106174 Mar. 9, 2020 1 Open Access Library Journal
Review of Research on Text Sentiment Analysis
Based on Deep Learning
Wenling Li
1
, Bo Jin
1
, Yu Quan
2*
1
College of Science, Yanbian University, Yanji, China
2
Department of Economics and Management of Yanbian University, Yanji, China
Abstract
Sentiment analysis is part of the field of natural language processing
(NLP),
and its purpose is to dig out the process of emotional tendencies by analyzing
some subjective texts. With the development of word vector, deep learning
develops rapidly in natural language processing. Therefore, the text
emotion
analysis based on deep learning has also been w
idely studied. This article is
mainly divided into two parts.
The first part briefly introduces the traditional
methods of sentiment analysis.
The second part introduces several typical
methods of sentiment analysis based on deep learning.
The advantages and
disadvantages of sentiment analysis are summarized and analyzed, which lays
a foundation for the in-depth research of scholars.
Subject Areas
Education
Keywords
Deep Learning, Sentiment Analysis, Convolutional Neural Network, Recurrent
Neural Network
1. Introduction
Text sentiment analysis is also known as opinion mining and tendency analysis.
In short, it is the process of analyzing, processing, inducing, and inferring sub-
jective text with emotion. It has a wide range of applications in public opinion
monitoring, stock and movie box office forecasting, and consumer preference
analysis [1]. Traditional affective analysis methods are mainly based on affective
dictionary and machine learning, but there are some difficulties in using these
two methods for affective analysis. Firstly, the text is unstructured. The length of
How to cite this paper:
Li, W.L., Jin
, B.
and
Quan, Y. (2020)
Review of Research on
Text Sentiment Analysis Based on Deep
Learning
.
Open Access Library Journal
,
7
:
e6174
.
https://doi.org/10.4236/oalib.1106174
Received:
February 17, 2020
Accepted:
March 6, 2020
Published:
March 9, 2020
Copyright © 20
20 by author(s) and Open
Access Library Inc
.
This work is licensed under
the Creative
Commons Attribution International
License (CC BY
4.0).
http://creativecommons.org/licenses/by/4.0/
Open Access
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