https://doi.org/10.31449/inf.v46i6.3991 Informatica 46 (2022) 33–44 33 
A Review and Comparative Analysis of Sentiment Analysis 
Techniques  
Shaha Al-Otaibi and Amal Al-Rasheed
*
 
E-mail: stalotaibi@pnu.edu.sa, aaalrasheed@pnu.edu.sa 
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint 
Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 
Overview paper 
Keywords: sentiment analysis, feature extraction, machine learning, social networking  
Received: February 8, 2022 
Social networking platforms have become a major source of information, which covers a wide range of 
topics and has gained a large volume of usage by people around the world. Platforms such as Twitter, 
Facebook, Instagram, and LinkedIn have attracted huge numbers of users who create public profiles and 
communicate with other users in the network. They exchange videos, posts, and comments. Social 
networking requires appropriate techniques to analyze the huge amount of complex, and frequently 
updated data generated. Sentiment Analysis is one such method of handling this vast volume of data and 
extracting useful knowledge from it. Social networking contents are analyzed using different techniques 
to gain insight from this data and use it in decision-making processes. The aim of this work is to study the 
sentiment analysis concept and present state-of-the-art techniques as well as provide a comparative study 
of these techniques. 
Povzetek: Pregledni članek opisuje metode za analizo mnenj v socialnih omrežij tipa Twitter, Faceook, 
Instagram in LinkedIn. 
 
1 Introduction  
Sentiment analysis has emerged as one of the most active 
research areas at the intersection of computer science and 
linguistics, with the goal of determining the emotions 
embedded in a text. Sentiment can be characterized as a 
positive, negative, or neutral evaluation that is stated 
through textual information, such as a review posted 
online for a movie, book, or product. It has become a 
common capability of most social networking platforms 
and is used by business, marketers, and political analysts. 
Sentiment analysis understands the expressed words and 
evaluates how language is used to convey human 
emotions [1]. 
Recently, social networking platforms have gained a 
high rate of usage by attracting lots of people, which has 
resulted in the generation of large volumes of data. 
However, appropriate techniques are required to analyze 
such large, complex, and frequently changing social media 
data. Additionally, to handle this vast volume of data and 
gain insight from it, sentiment analysis appears as a means 
to understand human attitudes and feelings. It contributes 
significantly to the decision-making process and related 
issues. For example, decision-making may include 
purchasing a product, making an investment, or exploring 
new ideas [2], and consumers are constantly interested in 
 
 
*
 Corresponding author 
learning from others' experiences while performing these 
activities. Nowadays, there are many reviews on social 
networks that can be used in sentiment analysis to 
understand the polarity of reviews, so that a user can 
quickly determine if a review is positive or negative 
without having to read the entire context, which helps in 
decision-making [3]. 
Sentiment analysis is commonly used in many 
domains such as, social networking platforms, marketing, 
customer service, clinical medicine, and healthcare. 
Among the important areas for sentiment analysis in 
health is the area of mental health with the use of 
persuasive technology [4]. Chatbots, for example, use 
sentiment analysis a lot for mental health analysis [5].  
Sentiment analysis is still a growing area of research 
and has a broad range of applications. Many studies have 
applied and discussed issues that influence sentiment 
analysis processing and affect its accuracy [6]. This work 
will review the sentiment analysis literature to investigate 
all the related aspects as well as present a comparative 
analysis of the recent studies in this domain. It introduces 
the social networking context and its framework and 
illustrates the position of sentiment analysis in this 
framework by providing the different processes and the 
related issues based on previous research. Many of the 
previous works have focused on aspects related to 
sentiment analysis while ignoring other aspects. The main 

34 Informatica 46 (2022) 33–44 S. Al-Otaibi et al. 
objective of this work is to present comprehensive 
research for sentiment analysis, covering the framework, 
processes, techniques, and datasets. We can express the 
overall contributions of this work as follows:  
1. It proposes a clear and precise description of the 
social networking and sentiment analysis contexts, as well 
as describing the framework and the relationship between 
them. 
2. It explains the different processes that are 
required to accomplish the sentiment analysis task.  
3. It presents the different techniques that have been 
applied in sentiment analysis, then reviews the state-of-
the-art research. 
4. It summarizes the results of each work and 
aggregates the analyzed output. 
5. It provides a comparative study based on select 
criteria to provide a guide for researchers to achieve good 
results.  
The rest of this paper is organized as follows: Section 
2 presents an overview of the social networking context 
and its framework, followed by an explanation of 
sentiment analysis and the feature extraction process in 
sections 3 and 4, respectively. Section 5 introduces the 
different techniques that have been applied in sentiment 
analysis. Next, section 6 presents an overview of the state-
of-the-art works that have been applied to sentiment 
analysis. The comparison between the different techniques 
of sentiment analysis is presented in section 7. Finally, the 
main conclusions drawn from this work are presented in 
section 8. 
2 Social networking platforms 
This section introduces the social networking analysis 
framework. Social networking can be defined as a set of 
platforms through which online users can communicate 
with each other. To identify a user’s interests and 
preferences, a user profile is created. Many approaches 
can be used to construct this profile, but the most used 
method is to allow the user to answer some questions. 
However, the user may not be willing to give information 
where the user’s interests change frequently [7]. In the last 
decade, the rapid change in technology has helped users to 
generate more and more content on social networking 
platforms. With the help of social networking, users share 
content related to influences, feelings, opinions, and 
sentiments, with the help of texts, pictures, video, and so 
on [8]. Examples of social networking platforms are 
Instagram, Snapchat, Facebook, and Twitter, where users 
generate vast volumes of data every day. We need to 
extract the useful knowledge from this data, such as 
understanding human feelings and opinions. Hence, social 
networking platforms are the more sophisticated source to 
find a large amount of data and can be handled to extract 
the required knowledge [6].   
Social Networking Analysis (SNA) is a new concept 
that provides techniques for characterizing and studying 
people’s interactions and the connections between them. 
Using SNA techniques, we can extract information from 
social networking platforms that might be useful for 
sentiment analysis, such as the sequence of influence 
among users or groups of similar users. It involves a three-
step process framework: capture, understand, and present 
[9]. An identify step must be integrated into this 
framework in order for postings or tweets to be identified 
prior to the capture step [10], as appears in Figure 1. This 
identification is achieved by utilizing the keywords 
specified by users. These keywords are then used in query 
requests to social networking analysis platform APIs, such 
as the Twitter API, to collect posts or tweets with the 
determined keywords. Therefore, the steps presented in 
[9], [10] and [11] are modified as follows: 
• The identify step involves finding the relevant 
keywords to be used in gathering social networking data. 
• The capture step utilizes the keywords to collect 
the relevant social networking data by checking different 
social networking sources, archiving related data, and 
extracting appropriate information.  
• The preprocessing step chooses the relevant data 
and removes noisy and low-quality data. This is followed 
by the feature extraction process. 
• The data analysis step employs different 
techniques to analyze the data and obtain insights from it.  
• The present step displays the findings in a 
meaningful way. 
3 Sentiment analysis 
The sentiment analysis process can be shown as a part of 
the social networking framework displayed in Figure 1. 
Hence, sentiment analysis is a process of verifying 
whether a piece of text about a particular entity, such as a 
product, review, movie, or tweet, is positive, negative, or 
neutral. It can be used to determine the consumer or social 
networking user’s attitude regarding this particular entity, 
using a set of variables. Enterprises can use sentiment 
analysis to discover public opinion of their products or 
services and to analyze customer satisfaction [3]. 
Additionally, they can use this analysis to collect crucial 
feedback about challenges in recently released products.  
When discussing sentiment analysis, we should 
mention opinion mining which has almost the same 
meaning. However, there is a small difference between 
them: opinion mining is the process of extracting and 
analyzing people’s opinion on a certain thing, whereas 
sentiment analysis is the process of looking for sentiment 
words or expressions and analyzing emotional feelings. 
The Merriam-Webster dictionary defines sentiment as “an 
attitude, thought, or judgment encouraged by feeling,” 
while it defines opinion as “a view, judgment established 
in the mind about a certain subject.” The distinction is 
slight, and they both contain specific characteristics of the 
other. According to the definitions, an opinion is a 
person’s point of view on a certain topic, whereas a 
 
Figure 1: Social networking platforms framework. 

A Review and Comparative Analysis of... Informatica 46 (2022) 33–44 35 
sentiment is about the person’s feelings. However, in the 
majority of cases, opinions reflect positive or negative 
sentiments, so sentiment analysis and opinion mining are 
nearly same thing and are used interchangeably in the data 
analytics literature [12]. 
Nowadays, most data are available in an unstructured 
format, hence the vast volume of data created by different 
channels such as chats, social networking, Reviews, 
articles, and documents. We need to analyze and 
understand these volumes of data to extract meaningful 
knowledge and benefit from it in the decision-making 
process. Sentiment analysis has emerged as a new 
technique for understanding people’s attitudes in different 
areas. It supports the processing of huge amounts of data 
in an efficient and cost-effective way. Moreover, 
sentiment analysis can identify vital issues in real-time, 
such as a public opinion crisis on social networking 
regarding certain products, services, or events. It can 
support the identification of such situations and discover 
the human trends and attitudes underlying them [13]. 
As mentioned above, the sentiment analysis process 
can be considered as part of the overall process 
represented in the social networking analysis framework 
presented in Figure 1. This framework can be extended to 
explore the sentiment analysis processes presented in 
Figure 2. Moreover, the sentiment analysis process has 
been applied at three levels: 1) Document level: at this 
level, the entire document is classified using sentiment 
analysis into positive, negative, or neutral results. 2) 
Sentence level: at this level, each sentence is evaluated to 
determine whether it reflects a positive, negative, or 
neutral result. This type of analysis is used for reviews and 
comments comprising one sentence written by a user in a 
social network. 3) Entity and Aspect level: sentiment 
analysis in this level is summarized based on the feature. 
The classification entails detecting and extracting specific 
features from the original data. This type is utilized in 
reviews when we seek sentiments on a specific aspect or 
feature [13]. For example, assume a buyer purchases a 
mobile phone and observes that the camera quality is 
good, but the sound quality is fair. Aspect level analysis 
can then be used to examine the many aspects of an entity. 
4 Feature extraction in sentiment 
analysis 
The feature extraction process is presented in this section. 
Sentiment analysis includes a sequence of processes such 
as data preprocessing, feature extraction, and 
classification, which are applied to find the polarity of 
data, as shown in Figure 2. Data preprocessing 
encompasses steps such as removing stop words, 
stemming, and performing tokenization. Stop words 
represent common words, such as is, am, are, in, to, which 
do not reveal any specific opinion. Stemming is a process 
of returning a word from different forms to its base. 
Tokenization is a process of dividing a sentence into 
separate words. Feature extraction is the process of 
converting the sentence into a bag of words that are 
represented by the features’ vector. Two methods can be 
applied to find the term occurrence in the corpus: term 
presence and term frequency. Term presence is the 
creation of a binary vector representing the extracted 
features, where 1 indicates that the term is available in the 
sentence and 0 represents the absence of the term. Term 
frequency is the creation of a feature vector representing 
the number of occurrences of the words. Term frequency 
can be applied using n-gram features, which are widely 
used in computational linguistics. As a result, the n-gram 
denotes the number of terms that appear in a text or speech 
corpus together. When only one term is used as a feature, 
it is called a unigram; when two terms are used, it is called 
a bigram, and so on. [13], [14]. 
5 Review of literature on sentiment 
analysis techniques 
Different types of techniques have been applied in 
sentiment analysis for social networking. These 
techniques can be categorized into three main categories: 
machine learning techniques, lexicon techniques, and 
hybrid techniques. The following sections present these 
categories and review the associated literature.  
5.1 Machine learning techniques   
Machine learning techniques can be further classified into 
three main classes: supervised, unsupervised, and deep 
learning techniques. The following subsections present 
these techniques and the different algorithms that have 
been applied in sentiment analysis. 
5.2 Supervised learning techniques 
Supervised learning techniques can be used upon the 
availability of labeled data for training the model. In this 
context, the two phases applied include training the model 
and the prediction of new cases [15]. In the training phase, 
the labeled data set is passed to the classification 
algorithm, which produces a model as an output. Then, the 
test data is passed to the model to predict the class of the 
new instances. These techniques have been applied to 
sentiment analysis; for example, several studies used 
emoticons and hashtags to create the training set, with the 
emoticons used as class labels to determine the polarity of 
 
Figure 2. The sentiment analysis process. 

36 Informatica 46 (2022) 33–44 S. Al-Otaibi et al. 
social networking posts [14]. Pang et al. proposed an 
algorithm that calculates the probability of every term in 
the training dataset, which extracts data from movie 
reviews to identify whether they are positive or negative. 
The algorithm classifies the new instance of data, then 
splits the newly classified sentence into separate word 
features. Next, the constructed model uses the 
probabilities calculated in the training phase to compute 
the conditional probabilities of the combined features in 
order to predict its class [16].  Additionally, the Bayesian 
Network algorithm was applied to enhance the aspect-
based sentiment analysis of reviews from Arabic hotels 
[17] . The terms’ dependency is represented as a directed 
graph, which is acyclic and contains a set of nodes 
representing the words as variables, and edges, which 
represent the dependency between these variables. The 
hyperplane is used by the Support Vector Machine (SVM) 
algorithm to offer the most separation between classes 
with the highest margin of hyperplane, lowering the upper 
bound of classification errors [16].  It can be used for 
dimensionality reduction and noise removal with various 
weighting systems such as term frequency–inverse 
document frequency (TF-IDF), term occurrence, and 
Binary Occurrence that uses chi-square as a feature 
selection [18]. Twitter data has been applied to interpret 
the insight hidden in public opinion. It was used to classify 
the sentiments of Twitter posts, in order to determine 
whether they were positive or negative. The unigram was 
also applied as a feature extraction method [3]. The 
Decision Tree algorithm has been applied on many types 
of datasets, where the training data is divided into smaller 
parts to identify patterns hidden in the data and used for 
the classification process [14].  Moreover, the Artificial 
Neural Network (ANN) is used for text classification 
using Reuters corpus documents as a training dataset in 
[19]. 
5.3 Unsupervised learning techniques 
These techniques are used when the available data is 
unlabeled; such data is easy to collect in some domain 
areas. The keywords are used to determine the sentences’ 
categories. Clustering algorithms are one of the solutions 
used to perform sentiment analysis in unlabeled data. 
These techniques can help to categorize the users’ 
sentiment texts into positive, negative, and neutral. 
Clustering algorithms were applied to perform clustering 
and deliver an appropriate number of clusters in an 
adequate run-time [14], [20].  Additionally, tweets were 
clustered into positive and negative tweets using the 
unsupervised approach based on the spectral clustering 
approach to perform sentiment analysis [21]. The 
clustering-based approach was developed to perform an 
aspect-based sentiment analysis that is concerned with 
gathering and categorizing general opinions about the 
features of a certain product or service. Many solutions for 
aspect clustering are monolingual and need to be applied 
in several languages. This algorithm categorizes the 
aspects related in semantic manner and performs the 
sentiment analysis in several languages [22]. 
5.4 Deep learning techniques   
Deep learning techniques refer to the multi-layer methods 
that adapt hidden layers of the neural network. These 
techniques differ from other machine learning techniques 
in how they deal with the feature extraction process. 
Supervised and unsupervised learning approaches extract 
features either manually or using a feature extraction 
method, while deep learning techniques learn and extract 
features in an automatic way to achieve a high accuracy in 
the learning process. Deep learning is currently one of the 
best solutions for many sentiment analysis problems. It 
can be classified into three main categories: Deep Neural 
Networks (DNN), Convolutional Neural Networks 
(CNN), and Recurrent Neural Networks (RNN). In 
addition, there are other methods such as the Recursive 
Neural Network, Belief Neural Network, and hybrid 
neural networks that are based on integrated techniques 
[23]. The DNN is a complex mathematical model with a 
set of layers; some of them are hidden layers that can 
process data in several ways. The DNN has three layers: 
an input layer that contains the input data, other hidden 
layers that contain processing nodes known as neurons, 
and an output layer that contains one or more neurons 
whose outputs are the network outputs [23], [24]. An 
enhancing DNN model has been applied to sentiment 
analysis for social networking, using a classifier combined 
with a word embedding model and a linear machine 
learning algorithm [25]. Additionally, the CNN is a 
specific type of neural network used in various areas such 
as Natural Language Processing (NLP), recommender 
systems, and computer vision. It comprises the 
convolutional and pooling or subsampling layers to 
deliver inputs to the classification layer [23].  It has been 
applied for handling a huge amount of unstructured data, 
which is a complex process. Feedforward neural networks 
with many hidden layers have been investigated using 
CNN [26]. The RNN is a type of neural network that has 
a directed cycle of connections between neurons. This 
connection structure produces feedback loops inside the 
RNN [27]. The RNN algorithm for sentiment analysis was 
proposed in [28] to recommend the places that are nearby 
to the user’s current position. It analyzed the different 
reviews extracted from different social networking sites.  
5.5 Lexicon techniques 
A lexicon can be defined as a set of predefined words, each 
of which has a polarity score associated with it. It is 
considered the simplest approach for sentiment analysis. 
In lexicon approaches, the classifier uses the lexicon 
directly and, matching each word with its polarity, 
classifies the entire sentence [20]. Many dictionaries are 
available online that provide information regarding the 
pinion words and their polarities. Popular lexicons include 
Sentiwordnet 3.0 (assigns to each sentiment of WordNet 
three sentiment scores: positivity, negativity, or 
objectivity), LIWC (Linguistic Inquiry and Word Count), 
and General Inquirer. Moreover, many datasets of words 
are manually created with determined semantic and 
cognitive categories [29], [14]. These techniques have 
been applied in sentiment analysis for text messages 

A Review and Comparative Analysis of... Informatica 46 (2022) 33–44 37 
posted on social networking platforms like Twitter [30]. 
The sentiment analysis of text is predicted based on the 
overall polarity of the words that constitute the messages. 
SentiStrength has been applied to classify the sentiment of 
textual messages. This algorithm is based on a lexicon that 
contains words and sentences that are used mostly in 
social networking [31].  Additionally, the lexicon is built 
by integrating three online dictionaries, saving only the 
occurrence words that are repeated. The seed words were 
selected using TF-IDF methods, then the word set was 
expanded using synonyms of the seeds to enhance 
classification performance [32]. The sentiment analysis 
algorithm was applied in Twitter and studied the patterns 
repeated in the data contexts. Then, the polarity and scores 
of words in the lexicons were determined by it [33].  
Moreover, semantic and statistical methods have been 
applied in sentiment analysis. Hence, the semantic 
orientation determines how positive or negative a word is. 
Esuli and Sebastiani have proposed a method that starts by 
expanding the initial seed words using the WordNet 
dictionary. Their assumption is that words with similar 
orientations are likely to have similar interpretations [34].  
Additionally, a statistical analysis for the collection of 
documents has used the K-Nearest Neighbors (KNN) 
algorithm. The model designs a classifier with several 
features: n-gram feature, pattern feature, punctuation 
feature, keyword-based feature, and word feature. It used 
the KNN classifier and calculates the accuracy of all 
algorithms. It focuses on classifying the tweets into 
positive and negative sentiments [35]. 
5.6 Hybrid techniques 
In much of the literature, two or more techniques have 
been combined to enhance the sentiment analysis process. 
In the following paragraphs, some of these hybrid 
techniques will be introduced. The algorithm proposed in 
[36] was designed to examine feedback on the same topic 
in order to derive useful contextual knowledge. Semantic 
similarity measurements were used to verify a given 
opinion’s semantic orientation. It took into account 
context-dependent opinions by employing linguistic rules 
to carry out semantic orientation of contextually 
dependent opinions. Then, in order to judge contextually 
dependent opinions, it collected contextual information 
from other reviews for the same product feature. Peng and 
Shih investigated an unsupervised learning method that 
uses rules of part-of-speech (POS) patterns to extract the 
sentiment phrases of each review. For each unknown 
sentiment phrase, they used each phrase as a query to find 
the top-N relevant snippets. After gathering the sentiment 
of the lexicon, the prediction sentiments of unknown 
emotion phrases were computed. The sentiments of 
adjacent known sentiment terms inside the snippets were 
then used to identify predictive sentiments. For opinion 
extraction, they only examined sentences that contained at 
least one detected sentiment phrase. Finally, the POS 
pattern was used to finish the opinion extraction [37]. 
Additionally, utilizing TF-IDF weight on the raw data, a 
technique based on the k-means clustering algorithm was 
devised. Here, a voting process was employed to obtain a 
stable clustering result. After that, the documents were 
divided into two groups: positive and negative [38].  The 
semantic orientation approach with other machine 
learning approaches, such as the n-gram model, has been 
used to analyze movie reviews [39].  The result gained by 
the machine learning approach was accurate; however, the 
model’s training process required a significant amount of 
time. Although less accurate, the semantic orientation 
technique is more efficient in real-time applications. The 
performance of the underlying POS tagger determines the 
performance of semantic orientation. In ref. [40], the 
authors used a hybrid method that combined a corpus-
based algorithm with SVM, resulting in good accuracy for 
light-stemmed data. Moreover, the three-stage cascade 
method that focuses on identifying the polarity shift 
problem in document level sentiment analysis was 
proposed in [41]. Each document was broken down into a 
set of subsentences. It created a hybrid model that 
combines certain principles with a statistical technique to 
find explicit and implicit polarity shifts. Then, to reduce 
polarity shift in negations, it presented a polarity shift 
exclusion approach. Finally, it trained the base classifiers 
on training subsets that were split by various categories of 
polarity shifts and used a weighted combination of the 
component classifiers for sentiment analysis. The hybrid 
algorithm was suggested in ref. [42] that combined the K-
means and DENCLUE (DENsity-based CLUstEring) 
methods to utilize the precise number of clusters and an 
adequate clustering performance, applying this to four 
different Twitter datasets.  
6 Recent techniques in sentiment 
analysis 
As presented above, the machine learning, lexicon, and 
hybrid approaches used to optimize these solutions can 
solve the sentiment analysis problem either as a 
classification or clustering problem. In this section, recent 
studies will be presented to show the new works that have 
been published in the area of sentiment analysis research. 
Currently, many approaches have been adapted to deal 
with sentiment analysis and tackle the large volume of 
data that is generated by different social networking 
platforms. In this section, some of the works that have 
been applied to sentiment analysis problems will be 
presented to understand and analyze users’ behaviors and 
attitudes regarding different online activities. Usually, 
social networking users send messages containing 
different multimodal contents. Such contents are generally 
brief and lack explicit sentiment words. To analyze the 
context of messages and understand the sentiment 
associated with them [31]. a model has been proposed that 
entails the semantic correlation between various 
modalities as well as the impacts of tweet context 
information.  
Many research works were applied to customer 
reviews created during online shopping. The study that 
looked at product reviews in support of consumers’ 
purchase decisions is presented in [43]. The authors of this 
study reviewed existing research on information fusion 
processes and methodologies. In the analysis stage, they 

38 Informatica 46 (2022) 33–44 S. Al-Otaibi et al. 
summarized the main conclusions that point to future 
research directions in this area. Additionally, a 
comparative study was conducted in an educational 
context, using three classifiers including an evolutionary 
algorithm. It aimed to mine the public opinions labeled 
with learning-centered emotions [44].  A new regression-
based approach was proposed to learn, analyze, and 
classify products and shop information based on customer 
experience. The data gathered from the benchmark of the 
Unified Computing System (UCS), which is a server for 
data-based computer products, aligns with a hardware 
evaluation, visualization support, and software 
management [45]. Furthermore, a sentiment analysis 
approach based on deep learning was used on product 
reviews acquired from Twitter. The TF-IDF weighted 
glove word embedding approach was integrated with the 
Convolutional Neural Networks (CNNs) method. This 
architecture consists of five tiers: a weighted embedding 
tier, convolution tier, max-pooling tier, followed by the 
LSTM and dense tiers [46]. 
Aspect-based sentiment analysis (ABSA) techniques, 
which anticipate the sentiment of specific aspects in a text, 
are also an increasing new trend. The recognition of the 
relevant contexts for different aspects is a challenge, hence 
the method was proposed for feature extraction networks 
to eliminate noise and enhance the aspect features for 
sentiment [47].  Additionally, the algorithm aggregates 
scores for the high occurrence class and the second class 
to predict the result score [48]. The predicted score is used 
to calculate the probability for each sentence to be part of 
a certain class from the five-star scale using a lexicon-
based system. The final probabilities are utilized to 
determine the overall sentiment at a document-level. 
Moreover, aspect-based sentiment analysis has been 
applied to identify both sentiments and aspects. The 
contextual word representations are obtained from the pre-
trained linguistic model named BERT (Bidirectional 
Encoder Representations from Transformers) [49]. 
One of the most attractive domains for sentiment 
analysis is analyzing traveler reviews. Sharing opinions 
between travelers can help people and decrease the risk of 
making bad decisions regarding their accommodation. 
Automatic review summarizations are a promising means 
of improving information processing of traveler reviews. 
The study conducted in [50] focused on extracting the 
relevant text features and performing sentiment analysis to 
compile review summaries. It proposed a systematic 
approach that used four classifiers to identify helpful 
reviews: Decision Tree (DT), Random Forest (RF), 
Logistic Regression (LR), and SVM. It then classified the 
sentences into six characteristics, which were 
subsequently examined to generate review summaries. 
Additionally, other research has explored the impact of 
traveler reviews to make direct endorsements in text. It 
applied a text mining approach to the online reviews to 
identify the effect of explicit recommendations [1].    
The deep learning model mentioned above can be 
considered a means to solving sentiment analysis 
problems. For example, in a recent study of Chinese 
language [31], the failure to fully utilize emotional 
information was identified as an issue. A single Chinese 
character might have multiple meanings in different 
words, and the character embeddings are combined with 
the word embeddings to obtain a more accurate meaning 
of the information. The CNN was used to recognize the 
sentiment classification of sequence semantic features. 
Thus, the suggested model for sentiment information 
extraction and analysis is based on a multi-neural network. 
Additionally, the deep neural networks model has been 
applied to evaluate the sentiment of text through 
combining the end-to-end memory neural network and 
termed Recurrent Memory Neural Network. The goal of 
this model is to address a weak interaction in the attention 
mechanism by creating a multielement attention 
mechanism that creates powerful attention weights and 
more accurate aspects based on sentiment representation 
[51]. Moreover, neural network research has considered 
the tweet-text information and user-connection 
information in Twitter data. However, the Attentional-
graph Neural Network based Twitter Sentiment Analyzer 
(AGN-TSA) has been proposed to perform sentiment 
analysis using attentional-graph neural networks. AGN-
TSA uses a three-layered neural structure that includes a 
word layer, a user layer, and an attentional graph network 
layer to merge Twitter text and user connection 
information [52]. 
Sentiment analysis for short text is challenging 
because of the lack of context. Song et al. addressed this 
issue by proposing a text representation model for short 
text sentiment analysis based on probabilistic linguistic 
terms sets (PLTSs). In this model, every word is 
represented as a PLTS that entirely describes the 
possibilities for the word’s sentiment polarity. They 
employed support vector machines and a polarity 
classification framework, which is a supervised and 
unsupervised learning technique [53].  In addition, a new 
study has presented a new approach for Twitter sentiment 
analysis that merges the results of standard classifiers and 
Natural Language Processing (NLP) techniques [54].  The 
proposed technique uses a fuzzy metric to decide the 
significance of each classifier. Hence, the fuzzy metrics 
are combined with the Choquet fuzzy to produce the final 
label. Furthermore, a machine learning technique based on 
a lexicon is proposed in ref. [55]. The polarity of the 
reviews is analyzed through negations, intensifiers, 
acronyms, and punctuation. Next, the words are used for 
computing the score, and the machine learning algorithms 
are used. 
Two well-known clustering algorithms, K-means and 
DENCLUE (DENsity-based CLUstEring), were 
combined in one model in [56] to exploit the accurate 
number of clusters from K-means and the clustering 
performance from DENCLUE. This model was applied to 
Twitter datasets and its effectiveness was tested against 
state-of-the art methods. 
Finally, sentiment analysis has been applied in many 
different situations to gain insight from people’s behavior 
and related feedback. For example, the research presented 
by Ruz et al. addressed the problem of sentiment analysis 
during critical events such as environmental disasters or 
social activities. They applied Bayesian network 
classifiers to implement sentiment analysis on Spanish 

A Review and Comparative Analysis of... Informatica 46 (2022) 33–44 39 
datasets [57]. Moreover, the ambiguity in political, 
religious, and social matters leads to extremism among 
people who are represented by their sentiments on social 
media platforms using English, which is the most 
commonly used language for sharing viewpoints on social 
media. Asif et al. studied the sentiment analysis of social 
media using multilingual textual data to determine the 
amount of extremist sentiment. They built a multilingual 
lexicon with different intensity weights. Then, the 
Multinomial Naive Bayes and Linear Support Vector 
Classifier algorithms were applied to perform the 
classification [58]. Authors in [59] , [60] studied COVID-
19 and recognized the strange situation of pressure that 
was considered on each country to establish plans to 
manage the population and utilize the existing resources 
in more convenient way. The data was collected from 
Twitter based on hashtags that including COVID-19, 
coronavirus, new case, deaths, recovered, and so on. The 
authors have proposed a hybrid heterogeneous Support 
Vector Machine approach. 
7 Comparative analysis and 
discussion 
As presented in the previous sections, many techniques 
have been applied by the researchers to solve sentiment 
analysis problems. The different techniques were analyzed 
and compared by showing their performance and the type 
of datasets, as presented in Table 1.  
Table 1: Summary of different sentiment analysis 
approaches. 
Ref. Source Techniques Domain Accuracy% 
[16] EMNLP (2002) Naïve Bayes Movie 
reviews 
81 
[17] Information 
Processing and 
Management 
(2019) 
Bayesian 
Network 
Hotel 
reviews  
59 
[18] I4CT (2014) SVM Movie 
reviews & 
SFU 
educationa
l review 
83 
[19] ICCIA (2016) ANN Reuters 
corpus 
documents 
68 
[41] Information 
Processing and 
Management 
(2016) 
SVM, 
logistic 
regression & 
Naïve Bayes 
Book, 
DVD, and 
Electronic
s reviews 
(Amazon.c
om) 
83.8, 84.6 
& 
85.6 
[21] International 
Journal of 
Computer 
Spectral 
clustering 
approach  
Twitter  
(Tweets 
about 
movies) 
72 
  
 
 
1 http://www.sananalytics.com/lab/twitter-sentiment/ 
2 http://www.ldc.upenn.edu/Catalog/ 
Applications 
(2016) 
 
[25] Expert System 
with 
applications 
(2017) 
 
DNN 
 
SemEval2
013, 
Seval2014
,  
Vader, 
STS-Gold,  
IMDB, 
PL04 
82.51, 
82.51,  
71.92, 
85.71,  
75.47, 
70.82 
 
[26] InAES (2017) CNN 
 
Twitter 75 
[28] 
 
CITS (2017) RNN 
  
Place 
reviews 
from 
different 
social 
networkin
g sites 
90.34 
[35] International 
Journal of 
Advanced 
Computer 
Science and 
Applications 
(2017) 
Corpus-
based using 
KNN 
Twitter 81 
[22] Knowledge-
Based Systems 
(2020) 
K-means & 
DENCLUE 
(DENsity-
based 
CLUstEring)   
Restaurant 
reviews 
(English, 
Dutch, 
Russian, 
Spanish & 
Turkish) 
71 
[32] SERA (2016) Lexicon-
based (3 
online 
dictionaries) 
Sanders 
Twitter 
Sentiment 
Corpus by 
Niek J. 
Sanders1 
58.11 
[33] Information 
Processing and 
Management 
(2016) 
Lexicon-
based with 
TF-IDF 
Twitter 87.5  
[34] 4th ACM 
International 
Conference on 
Information and 
Knowledge 
Management 
(2005) 
Corpus-
based with 
Cosine-
normalized 
TF-IDF 
HM2, 
TL3, & 
KA4  term 
sets 
61.18, 
68.53 & 
65.49 
[37] IEEE/WIC/AC
M (2010) 
Corpus-
based with  
Unsupervise
d snippet-
based 
sentiment 
Chinese U
GC 
corpus  
80 
3 http://www.altavista.com/ 
4 http://www.tasa.com/  

40 Informatica 46 (2022) 33–44 S. Al-Otaibi et al. 
classification
  
[38]   ISKE (2010) k-means & 
TF-IDF 
Movie 
reviews 
78.33 
[39] The 38th 
Hawaii 
International 
Conference on 
System Sciences 
(2005) 
N-gram 
Classifiers & 
Semantic 
Orientation 
approach 
Movie 
reviews 
85.54 
[40] AEECT (2013) Corpus-
based 
approach & 
SVM 
Twitter 87.2 
[3] International 
Journal of 
Advanced 
Computer 
Science and 
Applications 
(2018) 
SVM  Twitter 87 
[50] Tourism 
Management 
(2020) 
Decision 
Tree (DT),  
Random 
Forest (RF),  
Logistic 
Regression 
(LR), and 
SVM 
Online 
hotel 
reviews 
(TripAdvis
or.com) 
70.4,  
73.7,  
69.5 &  
63.7 
[44] Expert Systems 
with 
Applications 
(2020) 
EvoMSA  
Multinomial 
NB  
KNN  
Decision 
Tree  
B4MSA  
Bernoulli 
NB  
SVC  
Linear SVC  
Random 
Forest  
Deep 
Learning 
(LSTM  
CNN_5a 
Deep 
Learning 
CNN_10a  
CNN_LSTM
_7a  
BERT) 
sentiTEXT 
& 
eduSERE 
corpus 
93, 84  
87, 79 
79, 68 
85, 72 
92, 83  
87, 76  
90, 79  
90, 79  
89, 77  
89, 79  
91, 68  
90, 80  
88, 74  
93, 83 
[47] Complex and 
Intelligent 
Systems (2020) 
ML based 
regression 
model 
Product 
reviews 
98 
[61] Tsinghua 
Science and 
Technology 
(2020) 
Probability 
based model  
 
Twitter 73 
[45] 
 
Complex and 
Intelligent 
Systems (2020) 
Regression 
problem 
Product 
informatio
n from 
UCS 
98 
[46] Concurrency 
and 
Computation - 
Practice & 
Experience 
(2020) 
TF-IDF with 
CNN  
Twitter 87.12 
[51] Neurocomputin
g (2020) 
Recurrent 
Memory 
Neural 
Networks 
Twitter 94.09 
 
[1] Journal of 
Hospitality and 
Tourism 
Management 
(2020) 
Binary 
logistic 
regression 
Traveler 
reviews 
66.86 
[48] Journal of 
Information 
Science (2020) 
Lexicon-
based 
approach 
using Score 
Aggregation 
Hotel 
datasets 
from 
TripAdvis
or & 
restaurant 
reviews 
from 
CitySearch 
79 
[31] International 
Journal of 
Electrical 
Engineering & 
Education 
(2020) 
Multi-neural 
network 
JOYBUY 
user 
comment 
dataset for 
food 
88.13 
[57] Future 
Generation 
Computer 
Systems 
(2020) 
NB, TAN, 
BF TAN,  
SVM 
& RF 
Spanish 
datasets 
(Catalan 
independe
nce 
referendu
m) 
78.1, 80.8, 
80.8, 82.9 
& 85.8 
[58] Telematics and 
Informatics 
(2020) 
Linear SVC 
 
Multilingu
al dataset 
(acebook) 
82.1 
 
[53] Knowledge-
Based Systems 
(2020) 
SVM Movie 
reviews 
Stanford 
Twitter  
TripAdvis
or reviews 
76 
72 
78 
[54] Journal of 
Information 
Science (2020) 
Fusion + 
NLP 
Stanford, 
Stanford 
Twitter 
Sentiment, 
SemEval-
2016 
Movie 
Reviews 
81,  
83.6, 
66.6,  
83.8 
[55] International 
Journal of 
Negation-
intensifier-
Books 
dataset 
90.3 
 

A Review and Comparative Analysis of... Informatica 46 (2022) 33–44 41 
Technology and 
Human 
Interaction 
(2020) 
punctuation-
acronyms 
(NIPA) + 
SVM 
NIPA + 
Naïve Bayes 
 
(Taboada)  
91.3 
[56] Knowledge-
Based Systems 
(2020) 
K-means & 
DENCLUE 
(DENsity-
based 
CLUstEring) 
 
SemEval 
dataset for 
English, 
Dutch,  
Russian, 
Spanish, 
Turkish 
69, 61, 
63, 78, 51 
[52] Information 
(2020) 
Attentional-
graph Neural 
Network 
based 
Twitter 
Sentiment 
Analyzer 
(AGN-TSA) 
Twitter 94.62 
[49] 
 
22nd Nordic 
Conference 
(2019) 
Pre-trained 
linguistic 
model 
named 
BERT 
SemEval-
2016 
(Restauran
t, Laptop 
Reviews) 
 
96.3, 98.4 
[59] Inf Syst 
Front  (2021) 
Hybrid 
heterogeneo
us approach 
that applied 
SVM 
Twitter 
data, 
COVID19 
related 
data 
 96 
From Table 1, it has been observed that the accuracy 
of the sentiment analysis process depends upon the 
dataset, the selected features, and the classification or 
clustering algorithm that has been applied. Hence, the 
impact of data quality on the sentiment analysis process is 
affected by the high readability of the datasets. 
Additionally, most of studies consider the overall 
accuracy or F-score as a reliability metric and ignore the 
processing time. In such situation it is difficult to 
determine which technique has the best performance. 
However, when considering some algorithms that have 
been applied on Twitter data, we observed that the 
following algorithms gave the highest accuracy arranged 
from high to low: hybrid heterogeneous SVM, 
Attentional-graph Neural Network, Recurrent Memory 
Neural Networks, Corpus-based approach & SVM , TF-
IDF & CNN, SVM, Lexicon-based with TF-IDF, Corpus-
based using KNN, and CNN.   
We also observed that English is the most used 
language in sentiment analysis, but we found that some 
researchers have applied sentiment analysis for other 
languages, such as Arabic, Chinese, Dutch, Russian, 
Spanish, and Turkish. Additionally, the datasets used in 
sentiment analysis are mostly extracted from Twitter data, 
movie reviews, or traveler reviews. Therefore, sentiment 
analysis has mostly been applied in product and service 
reviews from business perspectives. Moreover, some new 
research has applied sentiment analysis to situations 
related to critical events, such as environmental disasters, 
social activities in social networking, or extremism. This 
presents the possibility of new research that applies 
sentiment analysis to many new situations related to 
different human activities and behaviors. 
8 Conclusion 
This paper discussed the different issues related to 
sentiment analysis. First, the social networking framework 
was presented, and the processes were determined, with 
an explanation of how the sentiment analysis process was 
integrated as a part of this framework. Then, the existing 
techniques that have been applied in sentiment analysis 
were explained in more detail. These techniques are 
generally divided into three main categories: machine 
learning, lexicon-based, and hybrid approaches. 
Additionally, sentiment analysis based on machine 
learning can be further classified into three categories: 
supervised machine learning, unsupervised machine 
learning, and deep learning techniques. The social 
networking content is textual data containing the 
emotional behavior of users, which can be analyzed and 
used in decision-making processes. The sentiment 
analysis of such content is performed using different 
techniques that classify social networking posts and 
reviews based on three main processes: preprocessing, 
feature extraction, and classification or clustering. These 
three processes have been applied in all techniques of 
sentiment analysis. Finally, a brief review was presented 
of the recent studies, and a comparative study of different 
works was provided based on a set of criteria including the 
technique used, domain, and accuracy.   
Acknowledgement 
Princess Nourah bint Abdulrahman University 
Researchers Supporting Project number 
(PNURSP2022R235), Princess Nourah bint Abdulrahman 
University, Riyadh, Saudi Arabia.  
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