https://doi.org/10.31449/inf.v45i7.3674 Informatica 45 (2021) 89–95 89 
Deep Learning Based Techniques for Sentiment Analysis: A Survey 
Wael Etaiwi 
King Talal School of Business Technology, Princess Sumaya University for Technology, Amman, Jordan 
E-mail: w.etaiwi@psut.edu.jo1 
 
Dima Suleiman 
King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan 
E-mail: dima.suleiman@ju.edu.jo2 
 
Arafat Awajan 
King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan 
College of Information Technology, Mutah University, AlKarak 
E-mail: Jordan, awajan@mutah.edu.jo4 
Overview Paper 
Keywords: natural nanguage processing, sentiment analysis, deep learning 
Received: August 8, 2021 
The automated representation of human language using a variety of techniques is called Natural 
Language Processing (NLP). Improvements to NLP applications are important and can be accomplished 
using a variety of methods, such as graphs, deep neural networks, and word embedding. Sentiment 
classification, which attempts to automatically classify opinionated text as positive, negative, or neutral, 
is a fundamental activity of sentiment analysis. Sentiment analysis methods focused on deep learning over 
the past five years are analyzed in this review. 
Povzetek: Opisan je kratek pregled metod globokega učenja za določanje razpoloženja iz besedil. 
 
1. Introduction
Sentiment analysis is an NLP application that extracts and 
analyzes the subjective information of the writer attitudes 
and opinions in various domains such as politics, 
sociology, business intelligence, etc. [1]. Moreover, the 
sentiment classification within certain domain could be 
about any event, item, topic, product, mobile application, 
or others. Sentiment analysis is important in many 
different tasks such as digital mental health [2] and 
recommender systems [3]. Furthermore, Sentiment 
analysis is also used in actual intelligent technologies for 
mental health, like intelligent cognitive assistants [4]. The 
main aim of sentiment analysis is to classify text into what 
is called polarity [5]. There are several forms of polarity, 
including the sentiment class, which can have several 
values, such as very positive, positive, neutral, negative 
and very negative, where the polarity values and classes 
differ from one study to another. Sentiment expressions 
such as happy, sad, angry, etc., in addition to sentiment 
classes, may be analyzed in sentiment analysis. In 
addition, text classification can be subjective or objective 
[6].  
Sentiment analysis approaches are classified intro 
three main groups [5]: Lexicon-based approaches, 
machine learning approaches, and hybrid approaches. In 
lexicon-based approaches, the polarity is extracted based 
on a predefined lexicon or dictionary. A sentiment 
dictionary refers to a list of words and their corresponding 
polarity value. While corpus is a dataset used to train the 
sentiment analysis model. On the other hand, in machine 
learning approaches, the computer is trained over a given 
dataset in order to build a classification model that could 
be used to extract the sentiment polarity of a given text. 
Finally, the hybrid approach combines both lexicon-based 
and machine learning technique to perform the sentiment 
analysis task. 
One of the main machine learning techniques is called 
a Neural Network (NN). NN is a programming paradigm 
that enables a computer to learn from observed data. NN 
is a biologically inspired structure that consists of a large 
number of interconnected processing units, called 
neurons, working to solve a specific problem or to enhance 
a specific application, such as image processing or pattern 
recognition. It consists of three main levels or layers (see 
Figure 1): the input layer, the hidden layer, and the output 
layer [7]. Neurons in the input layer (input neurons) are 
activated through sensing the environment, and then they 
produce activation values (weights) passed to neurons in 
the next layer and activate them. 

90 Informatica 45 (2021) 89–95 W. Etaiwi et al.  
Deep learning is a sub-field of machine learning that 
exploits many levels of representation (layers) that contain 
information processing units (neurons) for feature 
extraction and transformation, and pattern analysis and 
classification [8]. Deep learning provides better data 
representation that can make it easier to learn using 
machine learning techniques.  
Deep learning enhances different computer tasks, 
including text mining, image processing, and pattern 
recognition. By using deep learning, it is possible to 
represent words from textual data and produce word 
embedding that could be used by different machine 
learning techniques.  
Deep learning is used to learn features, rather than 
handcraft them. Handcrafting features is a time-
consuming process and is often incomplete. However, 
deep learning provides good features and multiple levels 
of representation that improve the efficiency of machine 
learning models. 
Several types of deep neural networks are used for 
NLP such as feedforward NN, Convolutional Neural 
Network (CNN), and Recurrent Neural Network (RNN). 
Deep learning outperforms other machine learning 
methods in several NLP tasks such as machine translation 
and named-entity recognition [9], [10]. Hence, several 
deep learning methods have been introduced to solve 
various NLP tasks. Yet supervised learning is the most 
widely used solution to NLP in recent deep learning 
studies [9]. 
The purpose of this paper is to provide a 
comprehensive survey of the sentiment analysis based on 
deep learning. The recent proposed deep learning-based 
approaches for sentiment analysis are further classified 
according to the type of deep learning network used. Three 
different categories are used: feed forward neural 
network-based sentiment analysis approaches, CNN based 
sentiment analysis approaches, and RNN based sentiment 
analysis approaches. Most of the proposed sentiment 
analysis surveys throughout the previous few decades 
 
1
 https://www.sciencedirect.com/ 
2
 https://dl.acm.org/ 
have been focused on the primary methodologies used to 
identify text polarity. Many surveys categorize the 
proposed sentiment analysis approaches as machine 
learning-based techniques, lexicon-based techniques, or 
hybrid strategies [5]. Because Deep Learning is growing 
and producing promising results in a variety of 
applications, the primary focus of this review is on Deep 
Learning-based techniques used for sentiment analysis. 
The remainder of this paper is structured as follows: 
section 2 discusses the methodology used to perform this 
review research; section 3 includes a discussion of the 
research analyzed for sentiment analysis using deep 
learning. In section 4, the analysis and conclusion are 
discussed. 
2. Methodology 
The researches available on the ScienceDirect
1
,  the ACM 
digital library
2
 and IEEE Xplore Digital library
3
 were used 
as data sources in this review. Specific keywords and their 
synonyms are used to select the articles to be includes in 
this review. The keywords selected are used to select the 
most suitable articles that fit the review topic. Deep 
learning-based sentiment, deep neural network sentiment, 
or multilayer neural network sentiment is a search string 
used in the paper search method. 
The search string is adjusted to significantly reduce 
the number of studies that are irrelevant. The search is 
limited for the last five years of publication. Journaled 
papers and conference or proceedings are among the 
publications included. The number of paper articles 
included is limited to those that target English text. 
When the paper article search stage is completed, a 
large number of papers that meet the criteria will be 
filtered out during the search adjustment process. The 
inclusion and exclusion criteria were established, and they 
were used to decide which papers to include or exclude 
from this review. Articles that use deep leaning neural 
network for sentiment analysis and explicitly discuss the 
key methodology, the dataset, the deep learning network 
type and structure, and the experimental evaluation meet 
the inclusion requirements. Journal and conference paper 
researches are included in this collection of articles. The 
chosen articles were published between 2015 and 2020. 
Articles that lack experimental evidence, a consistent 
dataset, a clear deep neural network architecture, and a 
clear methodology are among the exclusion criteria. 
Articles that focused at topics other than sentiment 
analysis and targeted languages other than English were 
also excluded. 
3. Literature review 
A range of research related to the study of sentiment using 
deep learning techniques is briefly discussed in this 
section. Sentiment analysis tasks can be carried out 
effectively by applying various models, such as deep 
learning models, which have recently been expanded. 
3
 https://ieeexplore.ieee.org/ 
 
Figure 1: Basic structure of neural network. 

Deep Learning Based Techniques for... Informatica 45 (2021) 89–95 91 
These models cover all various types of deep learning 
networks including: Feed Forward Neural Network 
(FFNN), Convolutional Neural Network (CNN), and 
Recurrent Neural Network (RNN). This section describes 
the efforts to integrate deep learning models for sentiment 
analysis by various researchers. 
4. Feed Forward Neural Network (FFNN) 
Feedforward NN is the simplest form of deep neural 
networks. It has a front propagated wave using a 
classifying activation function. In feedforward NN, there 
are no feedback connections in which the outputs of the 
model are fed back into itself. The information only travels 
forward in the neural network, from the input layer then 
through the hidden layers and finally through the output 
layer. Thus, all the data is inputted once in the input layer, 
and no data will be fed through. Therefore, for sentence 
manipulation, an input node is required for each word in 
the sentence. 
In their research [11], they studied the sentiment 
analysis of Arabic tweets because of the lack of 
applications that studies the Arabic sentiment since the 
Arabic language has different features. In their research, 
they proposed to use a hybrid approach. The first approach 
is the preprocessing which includes: stemming, 
tokenization, and stop word removal in addition to using 
two algorithms for weighting the features which are chai 
square and information gain. On the other hand, the 
second approach is the use of feed forward deep learning 
approach in Arabic tweets sentiment classification which 
used two polarities to classify the tweet into either positive 
or negative tweets. Moreover, comparisons were 
conducted using Arabic tweets and several classification 
methods such that: neural network, decision tree and 
support vector machine. As a conclusion, the hybrid 
approach achieved the highest results in terms of accuracy, 
precision with values 90% and 93.7% respectively. 
5. Convolutional Neural Network (CNN) 
A Convolutional Neural Network (CNN) is a neural-based 
approach that represents a feature function that extracts 
higher-level features [9]. Coevolutionary filters are 
applied to all possible window sizes in CNN in the input 
layer to produce a function map. After that, a max-pooling 
operation is applied on each filter to obtain a fixed-length 
output in order to reduce the output dimensionality. For 
word-level applications (such as word-based prediction), 
a window-based approach is used in CNN where the fixed-
size window of adjacent words is taken into account for 
each word in the input text. Then, in the center of the 
window, a standalone CNN is applied to the window to 
predict the term. In contextual windows, CNN is efficient 
in handling semantic clues, but fails to preserve sequential 
order and model long-distance contextual knowledge that 
is essential for different NLP tasks.  
The main difference between the ordinary neural 
network or FFNN and CNN is the number of layers [7]. 
CNN contains several numbers of convolutional layers 
with nonlinear activation functions. The ordinary neural 
network has fully connected layers in which each input 
neuron is connected to each output neuron in the next 
layer. However, in CNN, the output is computed using 
convolutions over the input layer. Therefore, each layer 
applies different filters (functions that analyze different 
features in the data [12]) and combine their results. 
CNN has been used in many sentiment analyses, 
however, in their work [13], they tried to use CNN filters 
with several lengths and grasp their power to find the 
polarity of the sentence in order to enhance the 
performance of the CNN classifier. Moreover, they used 
the Adaboost to combine several classifiers based on the 
size of the filter where the Adaboost can learn the 
classification error of weak classifier, thus the weak 
classifier will be combined with strong one in order to 
improve the final classification by adjusting the weights. 
Also, the experiments were made on Movie Review and 
IMDB using several N-grams. 
Convolutional neural networks (CNN) was not only 
used in text sentiment but it also was used in multimedia 
sentiments analysis which consists of text and images 
[14]. However, the input for CNN will be the combination 
of two individual CNN architectures, where one of them 
was used to learn the visual features and the other one was 
used to learn the text features. Thus, this helps in finding 
the internal relationship between the features. The 
experiments were made in two datasets: text sentiment 
(SentiStrength), visual sentiment (SentiBank) and the 
combination of the two, where the combined classifier of 
the two datasets provide the best results in predicting the 
sentiment. Moreover, the word embedding which was 
used to represent the text and images was skip-gram model 
and training was made in English Wikipedia corpus. In 
addition, comparisons were made between the proposed 
approach and several machine learning algorithms such 
as: SVM, Naïve Bays, and others. As a conclusion, the 
proposed CNN outperforms all other methods in term of 
accuracy with 79%, especially when used over the 
combined dataset. 
Different configuration settings of CNN were used by 
Rani and Kumar [15] in order to build a sentiment analysis 
model for Hindi movie reviews. The proposed model was 
trained over the 50% of the manually annotated dataset. 
The dataset was annotated by three native speakers of 
Hindi language. The experimental results were compared 
with the results of other machine learning approaches, and 
the comparison showed that the proposed CNN model 
overcome the performance of other state-of-the-art model 
and achieved an accuracy of 95%. 
Kapociute-Dzikiene et al. [16] applied CNN and 
LSTM deep learning neural networks to perform 
sentiment analysis on the Lithuanian Internet comment 
dataset. The proposed models were used on the top of both 
word2vec and FastText word embedding. The best result 
was reported using CNN with 70.6% of accuracy. The 
authors concluded that the deep learning neural networks 
demonstrated good results when applied to smaller 
dataset. 
A hybrid deep learning model for fine-grained 
prediction of sentiment in real-time multimodal data was 
proposed by Kumar et al. [14]. To deal with two unique 
semiotic systems, the authors combined deep learning 

92 Informatica 45 (2021) 89–95 W. Etaiwi et al.  
with machine learning: the textual (written text) and visual 
(still images) and their combination within the online 
content. There are four modules in the proposed model: 
discretization, text analysis, image analytics, and the 
decision module. Google Lens was used by the 
Discretization Module to separate text from images, which 
are then processed as separate entities and sent to the 
respective text analysis and image analysis modules. The 
Text Analytics Module explains the sentiment of using a 
CNN hybrid enriched with SentiCircle's contextual 
semantics. The 91% accuracy is obtained by the proposed 
model. 
6. Recurrent Neural Network (RNN) 
Recurrent Neural Network (RNN) is a neural-based 
approach that is effective in the processing of sequential 
information [9]. For each instance of the input sequence, 
RNN recursively uses the results previously computed. 
After being represented by a fixed-size token vector, the 
input sequences are fed sequentially (one by one) to the 
recurrent unit. The primary strength of an RNN is the 
ability to reuse previous computational results in future 
computations. Unlike FFNN, RNN makes it possible to 
feed input words through or feed input sequentially. RNN, 
on the other hand, continues to suffer from the issue of the 
vanishing gradient (changing previous results induces 
very minor changes in future computations), making it 
difficult to learn and modify the parameters in the earlier 
layers. [17]. 
The order of input data elements is a key factor in 
several applications [18]. For example, the word's order in 
a sentence can play a key role in NLP applications. 
Therefore, the need for some kind of memory to reserve 
the input data element order emerges. This is RNN's 
primary feature. The previous and the following terms 
may be important in NLP applications. Thus, when 
processing a word in such applications using two RNN 
layers and combining their outputs, it is important to look 
in both directions, forward and backwards. This 
mechanism is known as bidirectional RNN [19]. There are 
several different RNN types, such as Long Short-Term 
Memory (LSTM) [20] and Gated Recurrent Unit (GRU) 
[21]. In LSTM, for a short time during the learning 
process, the neurons are designed to retain, forget, or 
expose knowledge. Significant data can be preserved for 
longer by using LSTM, while irrelevant information can 
be forgotten. In GRU, relevant information is also retained 
using two main operations: reset and update, instead of 
three operations in LSTM. Hence, GRU performs as well 
as or better than LSTM. 
The bidirectional and unidirectional nature of three 
types of  RNN was considered in [22], where the three 
types are: GRU, LSTM and vanilla RNNs. Moreover, the 
performance evaluation of these three types was made 
using benchmark datasets SST-1 and SST-2 for sentiment 
analysis and health product review from Amazon, while 
the pre-training for word vector was made on Google 
News dataset. Accordingly, in term of accuracy, the GRU 
was the best. However, in case of the simple dataset and 
short reviews it is not recommended to use RNNs. 
A new neural network model was proposed in [23] 
which is called Attention-based LSTM model with the 
Aspect information (ALA). However, the new model was 
proposed to predict the sentiment score of financial 
opinion mining and to assign it to a certain pre-defined 
class. Therefore, there are layers of LSTM that take the 
word embedding as input and keep the intermediate output 
to relate them to the representation of final distribution. In 
addition, the dataset was used in the experiment contains 
675 microblog messages and 504 news headlines. Also, 
the sentiment score range between -1 and 1 and the 
number of classification labels were 27. As an input to the 
neural network, the word2vec model was used for word 
embedding. Moreover, the proposed model considered the 
aspect in order to precisely determine the word context. 
Accordingly, comparisons were made between several 
deep learning models and as a result, ALA got the best 
performance. 
In their research [24], they predict the sentiment and 
aspect of financial text using different deep learning 
models in their ensemble approaches which are: Recurrent 
Neural Networks (RNNs) and Convolutional Neural 
Networks (CNNs). Moreover, for prediction, they used 
voting and ridge regression at the last step, while they used 
the word embedding to convert the input and trained it 
using Twitter corpus. As a result, the performance of using 
CNNs was better than using RNNs on both sentiment and 
Aspect prediction. 
Chen et al. [25] proposed a sentiment analysis 
framework using a deep neural LSTM network in order to 
propose a self-developed military sentiment dictionary. 
The Militarylife PTT board of the largest online forum in 
Taiwan was used as the basis of the used dataset. The 
experimental results showed that the accuracy and F1-
measure of the proposed framework were higher than the 
results from using only existing dictionaries of sentiment. 
LSTM was used by Ghulam et al. [26] to build a 
sentiment analysis model for Roman Urdu language. The 
experimental results showed the significant accuracy with 
compare to other machine learning methods. 
Instead of having sentiment analysis at word or phrase 
level, in their research [27], they proposed a framework 
for sentiment analysis in document level. However, the 
input will be the document and the result will be an 
opinion or sentiment such as thumbs down and thumbs up. 
Therefore, in order to achieve document-level sentiment 
analysis, the document semantic representation is very 
crucial. Thus, they fragmented the semantics of the 
documents into four constitutes which are: representation 
of the word, the structure of the sentence, the composition 
of the sentence and the composition of the documents. The 
reason for this fragmentation is that, the meaning of the 
words and the rules that combine the words determine the 
semantics of the sentences and documents. On the other 
hand, the word embedding is used to represent the words 
to capture its meaning, but instead of using the standard 
word embedding, they used to learn sentiment specific 
word embedding (SSWE). The reason of using (SSWE) is 
to handle the problem of having sentiment words that are 
opposite in sentiment polarity but they are neighbors such 
as “good” and “bad” since they have similar context. 

Deep Learning Based Techniques for... Informatica 45 (2021) 89–95 93 
According to the structure of the sentence, the sentence 
representation is affected by the representation of its 
words. Finally, the sentence representation and the 
document composition are used to represent the semantics 
of the document.  
Sentiment Analysis of COVID-19 tweets were 
studied by Chakraborty et al. [28]. This study analyzed 
two types of tweets gathered during the pandemic times. 
Deep learning was used to validate the author’s thoughts 
that people have tweeted positive regards COVID-19. In 
this work, two sets of datasets were used. Although one 
dataset contains all the tweets that were released during 
the timeframe from December 2019 to May 2020, the 
second collection of tweets that were most retweeted were 
given priority. 
Arabic sentiment classification for sentence level was 
the main focus in [29]. In their work, Al Sallab and his 
colleagues proposed a deep learning framework which 
explored four several architectures. The first three 
architectures based on Bag-of-Words as an input along 
with features extracted from the Arabic sentiment lexicon. 
Thus, the three models were based on Deep Auto-
Encoders (DAE) and Deep Belief Networks (DBN). On 
the other hand, the fourth architecture was used to handle 
the shortage of context handling in previous three 
architectures which based on Recursive Auto Encoder 
(RAE). As a result, the fourth architecture was the best and 
had many advantages in term of no need to have a lexicon 
as a resource.  
Deep Neural Network (DNN) used several layers of 
conventional neural network and applied back 
propagation, while DBN used the fine-tuning step but 
before using it, it applied pre-training. On the other hand, 
DAE reduces the dimensionality of the original model 
representation. However, RAE minimized the input data, 
probability by parsing the input words in the best order by 
exploring the best parse tree. Therefore, the result is the 
reorder of the input sentences in order to compact its 
representation. Also, this is the same result that was 
provided using DAE where the objective of both models 
was to minimize the reconstruction error. On the other 
side, the difference between the two models was that DAE 
can deal only with a vector that is fixed length, while RAE 
can parse sentences with variable length. However, both 
models based on unsupervised learning, and both of them 
require classifier at the top which was DBN in case of 
DAE and Softmax in case of the RAE. The experimental 
results showed that the F1 measure score was improved by 
9% over all other literature results. 
7. Discussion and conclusion 
Deep learning is made up of a number of popular and 
effective models that are used to tackle a wide range of 
issues. In this review, various studies are reviewed in order 
to provide a thorough understanding of the successful 
growth of deep learning applications in the field of 
sentiment analysis. It is obvious from this survey that the 
work on applying deep neural networks for sentiment 
Paper Year 
Neural Network 
Model 
Dataset Results/Evaluation  
[11] 2017 FF Arabic tweet 
Accuracy, precision with 90% 
and 93.7% respectively. 
[13] 2016 CNN Movie Review and IMDB  
[14] 2015 CNN 
SentiStrength (text) 
SentiBank (visual) 
Accuracy 79% 
[15] 2018 CNN Manually annotated dataset Accuracy 95% 
[30] 2020 CNN 8000 comments and posts Accuracy 90.9% 
[16] 2019 
CNN and RNN-
LSTM 
Lithuanian Internet 
comments 
Accuracy 70.6% 
[22] 2017 RNN 
Amazon health product 
reviews, SST-1 and SST-2 
In term of accuracy, GRU is the 
best 
[23] 2018 RNN-LSTM 
504 news headlines and 
675 microblog messages 
 
[24] 2018 RNN 
Twitter posts and news 
headlines in the financial 
domain. 
 
[25] 2019 RNN-LSTM Militarylife PTT 
Accuracy 85.4% 
F1-measure 88.41% 
[26] 2019 RNN-LSTM Roman Urdu dataset Accuracy 95.2% 
[27] 2015 Word embedding SemEval 2013  
[28] 2020 Word embedding Tweets Accuracy 62.8% 
[29] 2015 Auto-encoder 
Linguistic Data Consortium 
Arabic Tree Bank 
Accuracy 73.5% 
Table 1: Recently proposed sentiment analysis approaches. 

94 Informatica 45 (2021) 89–95 W. Etaiwi et al.  
analysis is receiving growing attention in recent years. 
Table 1 summarizes recent papers on the various types of 
deep learning neural networks to construct sentiment 
analysis models in the last five years. The third column in 
Table 1 indicates that, various types of deep-learning 
neural networks could be used to achieve good results in 
sentiment analysis. The fourth column shows that there are 
several different datasets can be used to evaluate the 
proposed models. The lack of a single or benchmark 
dataset for this purpose makes it a difficult task to compare 
the different models together. A number of datasets is 
available for sentiment analysis, but the datasets used for 
deep learning are vary. On the other hand, the accuracy 
achieved by the listed researches are slightly different; the 
preferences for one module over another depends on the 
size of the dataset and the parameters of deep neural 
network. After reviewing all of these studies, it has been 
shown that sentiment analysis can be done more 
efficiently and accurately utilizing deep learning 
approaches. Deep learning networks perform autonomous 
feature extraction without the need for human interaction, 
which saves time by eliminating the requirement for 
feature engineering. Deep learning, on the other hand, 
necessitates a considerable amount of data as compared to 
other machine learning algorithms, which becomes its 
primary limitation. Word embeddings clearly played an 
important part in deep learning-based sentiment analysis 
algorithms. It is also clear that word embeddings can be 
used as features in non-neural learning models for a range 
of tasks that don not even require deep learning. This 
review presents a comprehensive survey of using deep 
learning for sentiment analysis. The studies are analyzed 
and classified according to the type of deep neural network 
used in the sentiment analysis models. 
8. References 
[1] G. Beigi, X. Hu, R. Maciejewski, and H. Liu, “An 
Overview of Sentiment Analysis in Social Media and 
Its Applications in Disaster Relief,” in Sentiment 
Analysis and Ontology Engineering, Springer, 
Cham, 2016, pp. 313–340. Accessed: Jun. 02, 2018. 
https://doi.org/10.1007/978-3-319-30319-2_13 
[2] T. Kolenik and M. Gams, “Intelligent Cognitive 
Assistants for Attitude and Behavior Change Support 
in Mental Health: State-of-the-Art Technical 
Review,” Electronics, vol. 10, no. 11, p. 1250, Jan. 
2021 
https://doi.org/10.3390/electronics10111250. 
[3] B. Alhijawi and A. Awajan, “Prediction of Movie 
Success using Twitter Temporal Mining,” in 
Proceedings of the Sixth International Congress on 
Information and Communication Technology: 
ICICT 2021, London, vol. 1.  
https://doi.org/10.1007/978-981-16-2377-6_12 
[4] T. Kolenik and M. Gams, “Persuasive Technology 
for Mental Health: One Step Closer to (Mental 
Health Care) Equality?,” IEEE Technol. Soc. Mag., 
vol. 40, no. 1, pp. 80–86, Mar. 2021.  
https://doi.org/10.1109/MTS.2021.3056288. 
[5] M. Biltawi, W. Etaiwi, S. Tedmori, A. Hudaib, and 
A. Awajan, “Sentiment classification techniques for 
Arabic language: A survey,” 2016 7th Int. Conf. Inf. 
Commun. Syst. ICICS 2016, pp. 339–346, 2016. 
https://doi.org/10.1109/IACS.2016.7476075 
[6] V. Rajput and S. Dubey, “An Overview of Use of 
Natural Language Processing in Sentiment Analysis 
based on User Opinions,” Int. J. Adv. Res. Comput. 
Sci. Softw. Eng., vol. 6, no. 4, 2016. 
[7] M. M. Lopez and J. Kalita, “Deep Learning applied 
to NLP,” CoRR, vol. abs/1703.03091, 2017. 
[8] L. Deng, “Deep Learning: Methods and 
Applications,” Found. Trends® Signal Process., vol. 
7, no. 3–4, pp. 197–387, 2014,  
https://doi.org/10.1561/2000000039. 
[9] T. Young, D. Hazarika, S. Poria, and E. Cambria, 
“Recent trends in deep learning based natural 
language processing,” Ieee Comput. Intell. Mag., 
vol. 13, no. 3, pp. 55–75, 2018.  
https://doi.org/10.0.4.85/MCI.2018.2840738 
[10] L. Deng and Y. Liu, Eds., Deep Learning in Natural 
Language Processing. Springer Singapore, 2018. 
https://doi.org/10.1007/978-981-10-5209-5 
[11] A. Altaher, “Hybrid approach for sentiment analysis 
of Arabic tweets based on deep learning model and 
features weighting,” Int. J. Adv. Appl. Sci., vol. 4, 
no. 8, pp. 43–49, Aug. 2017.  
https://doi.org/10.21833/ijaas.2017.08.007. 
[12] Y. LeCun, Y. Bengio, and others, “Convolutional 
networks for images, speech, and time series,” 
Handb. Brain Theory Neural Netw., vol. 3361, no. 
10, p. 1995, 1995.  
https://doi.org/10.5555/303568.303704 
[13] Y. Gao, W. Rong, Y. Shen, and Z. Xiong, 
“Convolutional Neural Network based sentiment 
analysis using Adaboost combination,” in 2016 
International Joint Conference on Neural Networks 
(IJCNN), Jul. 2016, pp. 1333–1338.  
https://doi.org/10.1109/IJCNN.2016.7727352. 
[14] G. Cai and B. Xia, “Convolutional Neural Networks 
for Multimedia Sentiment Analysis,” in Natural 
Language Processing and Chinese Computing, 
Springer, Cham, 2015, pp. 159–167. Accessed: Jun. 
01, 2018.  
https://doi.org/10.1007/978-3-319-25207-0_14 
[15] S. Rani and P. Kumar, “Deep Learning Based 
Sentiment Analysis Using Convolution Neural 
Network,” Arab. J. Sci. Eng., vol. 44, no. 4, pp. 
3305–3314, Aug. 2018,  
https://doi.org/10.1007/s13369-018-3500-z. 
[16] J. Kapociute-Dzikiene, R. Damaševičius, and M. 
Woźniak, “Sentiment Analysis of Lithuanian Texts 
Using Traditional and Deep Learning Approaches,” 
Comput., vol. 8, p. 4, 2019.  
https://doi.org/10.3390/computers8010004 
[17] P. Le and W. Zuidema, “Quantifying the Vanishing 
Gradient and Long Distance Dependency Problem in 
Recursive Neural Networks and Recursive LSTMs,” 
in Proceedings of the 1st Workshop on 
Representation Learning for NLP, Berlin, Germany, 

Deep Learning Based Techniques for... Informatica 45 (2021) 89–95 95 
Aug. 2016, pp. 87–93.  
https://doi.org/10.18653/v1/W16-1610. 
[18] J. Schmidhuber, “Deep learning in neural networks: 
An overview,” Neural Netw., vol. 61, pp. 85–117, 
Jan. 2015.  
https://doi.org/10.1016/j.neunet.2014.09.003. 
[19] M. Schuster and K. K. Paliwal, “Bidirectional 
Recurrent Neural Networks,” Trans Sig Proc, vol. 
45, no. 11, pp. 2673–2681, Nov. 1997.  
https://doi.org/10.1109/78.650093. 
[20] S. Hochreiter and J. Schmidhuber, “Long short-term 
memory,” Neural Comput., vol. 9, no. 8, pp. 1735–
1780, 1997.  
https://doi.org/10.1162/neco.1997.9.8.1735 
[21] K. Cho, B. van Merriënboer, D. Bahdanau, and Y. 
Bengio, “On the Properties of Neural Machine 
Translation: Encoder–Decoder Approaches,” in 
Proceedings of SSST-8, Eighth Workshop on 
Syntax, Semantics and Structure in Statistical 
Translation, Doha, Qatar, Oct. 2014, pp. 103–111.  
https://doi.org/10.3115/v1/W14-4012. 
[22] K. Baktha and B. K. Tripathy, “Investigation of 
recurrent neural networks in the field of sentiment 
analysis,” in 2017 International Conference on 
Communication and Signal Processing (ICCSP), 
Apr. 2017, pp. 2047–2050.   
https://doi.org/10.1109/ICCSP.2017.8286763. 
[23] S. E., L. Yang, M. Zhang, and Y. Xiang, “Aspect-
based Financial Sentiment Analysis with Deep 
Neural Networks,” in Companion Proceedings of the 
The Web Conference 2018, Republic and Canton of 
Geneva, Switzerland, 2018, pp. 1951–1954.   
https://doi.org/10.1145/3184558.3191825. 
[24] G. Piao and J. G. Breslin, “Financial Aspect and 
Sentiment Predictions with Deep Neural Networks: 
An Ensemble Approach,” in Companion of the The 
Web Conference 2018 on The Web Conference 
2018, 2018, pp. 1973–1977.  
https://doi.org/10.1145/3184558.3191829 
[25] L.-C. Chen, C.-M. Lee, and M.-Y. Chen, 
“Exploration of social media for sentiment analysis 
using deep learning,” Soft Comput., vol. 24, no. 11, 
pp. 8187–8197, Oct. 2019.  
https://doi.org/10.1007/s00500-019-04402-8. 
[26] H. Ghulam, F. Zeng, W. Li, and Y. Xiao, “Deep 
Learning-Based Sentiment Analysis for Roman 
Urdu Text,” Procedia Comput. Sci., vol. 147, pp. 
131–135, 2019. 
https://doi.org/10.1016/j.procs.2019.01.202. 
[27] D. Tang, “Sentiment-Specific Representation 
Learning for Document-Level Sentiment Analysis,” 
in Proceedings of the Eighth ACM International 
Conference on Web Search and Data Mining, New 
York, NY, USA, 2015, pp. 447–452.   
https://doi.org/10.1145/2684822.2697035. 
[28] K. Chakraborty, S. Bhatia, S. Bhattacharyya, J. 
Platos, R. Bag, and A. E. Hassanien, “Sentiment 
Analysis of COVID-19 tweets by Deep Learning 
Classifiers—A study to show how popularity is 
affecting accuracy in social media,” Appl. Soft 
Comput., vol. 97, p. 106754, 2020.  
https://doi.org/10.1016/j.asoc.2020.106754. 
[29] A. Al Sallab, H. Hajj, G. Badaro, R. Baly, W. El Hajj, 
and K. B. Shaban, “Deep learning models for 
sentiment analysis in Arabic,” in Proceedings of the 
Second Workshop on Arabic Natural Language 
Processing, 2015, pp. 9–17.  
https://doi.org/10.18653/v1/W15-3202 
[30] A. Kumar, K. Srinivasan, W.-H. Cheng, and A. Y. 
Zomaya, “Hybrid context enriched deep learning 
model for fine-grained sentiment analysis in textual 
and visual semiotic modality social data,” Inf. 
Process. Manag., vol. 57, no. 1, p. 102141, 2020. 
https://doi.org/10.1016/j.ipm.2019.102141.
 
  

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