https://doi.org/10.31449/inf.v47i5.4673 Informatica 47 (2023) 153–158 153 
Sentiment Analysis of Financial Textual data Using Machine 
Learning and Deep Learning Models 
 
Hero O. Ahmad
1
, Shahla U. Umar
2 
1
Presidency of the University of Kirkuk, University of Kirkuk, Iraq 
2
College of Computer Science and Information Technology, University of Kirkuk, Iraq 
E-mail: heroomarahmad1984@gmail.com 
Keywords: social media comments, MNB, LR, RNN, LSTM, GRU 
Received: February 9, 2023 
Recently, extensive research in the field of financial sentiment analysis has been conducted. Sentiment 
analysis (SA) of any text data denotes the feelings and attitudes of the individual on particular topics or 
products. It applies statistical approaches with artificial intelligence (AI) algorithms to extract 
substantial knowledge from a huge amount of data. This study extracts the Sentiment polarity (negative, 
positive, and neutral) from financial textual data using machine learning and deep learning algorithms. 
The constructed machine learning model used Multinomial Naïve Bayes (MNB) and Logistic regression 
(LR) classifiers. On the other hand, three deep learning algorithms have been utilized which are 
Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit 
(GRU). The results of the MNB and LR obtained a good and very good rate of accuracy respectively. 
Likewise, the results of RNN, LSTM, and GRU obtained an excellent rate of accuracy. It can be 
concluded from the outcomes that the used preprocessing stages made a positive impact on the accuracy 
rate. 
Povzetek: Raziskava analizira finančna besedila z uporabo klasičnega strojnega učenja in globokega 
učenja z namenom iskanja sentimenta (negativno, pozitivno, nevtralno). 
 
1   Introduction 
Recently, the advancement of technology produces an 
enormous number of social media users that generate huge 
data [1]. The data of the users consist of a huge number of 
writings in the form of text comments. This constructive 
information from social media is an emerging area for 
research [2]. The term "Deep Learning" (DL) pertains to 
the latest advancement in technology and current research 
emphasis in the field of Machine Learning. DL has 
become ubiquitous in our daily lives, offering solutions 
that were once considered the subject matter of science 
[3]. The widespread use of machine learning and deep 
learning has made it possible to apply them in various 
fields, such as computer vision, machine translation, 
recommendation systems, cybersecurity, and sentiment 
analysis [4]. Sentiment analysis (SA) is 
also called Opinion analysis or Opinion mining, it is a 
subfield of natural language processing (NLP) that 
evaluates the degree of polarity in the sentence to analyze 
and extracts feelings from text data. Several types of 
research have been carried out by establishments for 
finding people's sense of a given matter [5]- [6]. The 
polarity involves determining the attitude or emotion 
expressed in a piece of text, which can be positive, 
negative, or neutral. It is a subfield of text classification, 
which involves analyzing people's opinions, emotions, 
and attitudes towards entities and their characteristics as 
expressed in a written text [7]. SA can be challenging 
because it can be difficult for humans to determine the 
emotion behind the text. Additionally, a single text can 
contain multiple emotions, which can make it difficult to 
achieve high accuracy in the analysis process. Hence, 
identifying the appropriate features or markers to 
effectively distinguish between different classes is a 
significant obstacle in any classification task that involves 
text [8] [9]. Assessing the emotions expressed through 
digital channels like financial news comments can be 
advantageous for creating trading plans. Furthermore, the 
emotional content conveyed through financial news has 
the potential to predict future trends, which can be 
beneficial for those managing portfolios and risks [10]. 
Furthermore, SA can be conducted at three levels which 
are the aspect level, the sentence level, and the document 
level. With the rise of social media and other platforms 
that allow users to share their thoughts and feelings about 
various things, such as entities, products, people, and 
organizations, it has become possible to analyze the 
sentiments expressed in these reviews and other online 
content [11]. Throughout the last decade, Sentiment 
analysis methods have grown greatly and evolved from 
basic statistics rules to advanced machine learning 
methods such as deep learning, which has become an 
outstanding technology in various NLP projects. 
Likewise, these machine and deep learning systems have 
achieved impactful outcomes on the SA data [12]. Deep 
learning is a method of representation learning that uses 
nonlinear neural networks to learn multiple levels of 
representation. These representations are created by 
transforming the representation at one level into a more 
abstract representation at a higher level. These learned 
representations can be used as features in detection or 
classification jobs [13]. The main goal when using 
classification algorithms is to properly prepare and 
preprocess the dataset and, ideally, use a large number of 
data points to train the model. Different supervised and 

154 Informatica 47 (2023) 153–158 H.O. Ahmad et al. 
unsupervised algorithms with their parameters can be 
tested for finding the sentiment to achieve the best results 
as shown in figure 1 [14]. Hence, SA can be used in a 
variety of contexts beyond just product reviews, such as in 
the analysis of stock markets, news articles, political 
debates, advertising, elections, etc. [15].  
 
 
Figure 1: Organization of sentiment analysis [16]. 
 
In this study, the aim is to extract the sentiment 
polarity from financial textual data using both machine 
learning and deep learning algorithms. Specifically, 
Multinomial Naïve Bayes (MNB) and Logistic regression 
(LR) classifiers ar e used as machine learning m o d els and 
recurrent neural networks (RNN), long short-term 
memory (LSTM), and gated recurrent units (GRU) as deep 
learning models. These models can capture the contextual 
dependencies and temporal dependencies present in text 
data, which are crucial for accurately identifying the 
sentiment expressed in a given text. We also examine the 
impact of the utilized preprocessing stages on the model’s 
accuracy and also on the heterogeneous data collected 
from different sources.  
Overall, this research aims to contribute to the 
growing body of knowledge on sentiment analysis in the 
field of finance from social media and provide insights 
into the effectiveness of different machine learning and 
deep learning approaches for this task. 
       This article is organized as follows: Section two 
explains related work for sentiment analysis, Section three 
illustrates the proposed system architecture, Section four 
portrays the experimental and results, and Section five 
demonstrates the discussion. Finally, the conclusion and 
future work are in section 6. 
 
2   Related work 
Nowadays, many articles concentrated on addressing the 
problem of sentiment analysis classification by using 
different techniques for classifying the sentiment of 
individuals at several stages.  In this section, the focus will 
be on the latest contributions of this field. 
G. Mostafa et al, 2021 [17] Used a few algorithms of 
machine learning on the Twitter data by utilizing many 
steps of preprocessing and encoding methods for 
increasing the accuracy rate. Then a comparison among 
the attained accuracies was presented. Their experiments 
demonstrate that the Neural Network algorithm offers 
outstanding accuracy compared with other algorithms. A. 
Al Shamsi et al, 2022 [18] Constructed the Emirati dialect 
dataset for the Instagram platform according to three 
comment polarities. They evaluate the quality of their 
corpus utilizing Cohen’s kappa coefficient and also 
assessed the quality of the corpus by employing eight 
machine learning algorithms. Finally, they compared the 
performance of the utilized algorithms to find out the 
accurate classifiers. A. FiIzollah et al, 2019 [19] 
concentrated on halal products from the Twitter dataset to 
extract sentiment polarity from the tweets by utilizing 
deep learning in English and Malay languages. The 
highest accuracy results were scored by using CNN and 
LSTM algorithms. B. Fazlija et al, 2022 [20] discovered 
the financial sentiment knowledge from the news article. 
Then apply the predicted sentiment scores to estimate the 
stock market price direction by using the BERT algorithm. 
H. Fouadi et al, 2021 [21] Their work attempts to compare 
the performance of Arabic sentiment analysis by 
employing several machines and deep learning algorithms 
to extract the polarity of the sentiment. The machine 
learning algorithms include Support Vector Machines 
(SVM), Logistic Regression (LR), and K-Nearest 
Neighbors (KNN). The deep learning algorithm is used the 
Long Short-Term Memory (LSTM) model. These 
algorithms are applied to a dataset called the Arabic 
Review, which consists of manually annotated text data 
collected from various Arabic sources. H. Shehu et al, 
2021 [22] Their study uses three techniques to expand the 
size of the training data: Shift, Shuffle, and Hybrid. Then, 
we employ three deep learning models which are recurrent 
neural network (RNN), convolution neural network 
(CNN), and hierarchical attention network (HAN) to 
classify stemmed Turkish data in Twitter for sentiment 
analysis and compare the performance of these models to 
traditional machine learning models. S. Liu et al, 2020 
[23] evaluated the ability of various machine and deep 
learning models to predict user sentiment polarities and 
found that certain techniques, such as using binary bag-of-
word, incorporating bi-grams, and normalizing text, 
improved the performance of machine learning models. 
For deep learning models, discovered that using pre-
trained word embeddings and limiting maximum length 
often enhanced model performance. Also, he found that 
simpler models such as LR and SVM were more effective 
at predicting sentiments than more complex models like 
Gradient Boosting, LSTM, and BERT. G. Kaur1 et al, 
2023 [24] introduce a method for sentiment analysis that 
combines different approaches. The process involves three 
main steps: pre-processing, feature extraction, and 
sentiment classification. To eliminate unwanted data from 
text reviews, the pre-processing stage uses NLP 
techniques. To extract features effectively, the authors 
introduce a hybrid approach that combines review-related 
and aspect-related features to create a unique hybrid 
feature vector for each review. Finally, sentiment 
classification is carried out using LSTM deep learning 
classifier. 
 
The main goal of this research is to expand the 
existing knowledge about sentiment analysis in the field 
of finance, particularly from social media sources. 
Additionally, the study aims to provide valuable insights 

Sentiment Analysis of Financial Textual data Using Machine Learning… Informatica 47 (2023) 153–158 155 
into the effectiveness of various machine learning and 
deep learning techniques for this specific task and then 
find out the most suitable algorithm for this area. 
However, testing the impact of various preprocessing 
steps in the process of increasing the accuracy in each 
model. 
 
 
3   Proposed system architecture 
In this section, the proposed system framework for 
financial sentiment analysis using machine learning and 
deep learning algorithms is outlined. The system 
architecture includes five types of algorithms, the overall 
framework follows a set of steps to achieve its goal and it 
is depicted in Figure 2. As can be seen, the proposed 
system steps consist of six stages: 
 
 
1- Data Collection: Financial data is collected from 
multiple social media sources, including Facebook, 
Twitter, and financial blogs.  
2- Data Aggregation: The data is aggregated and 
filtered by using a Python algorithm for detecting 
and collecting social comments in the English 
language only. 
3- Preprocessing: The collected data undergoes 
several preprocessing steps to improve the accuracy 
of the models. These steps include feature 
selection, data cleaning, data balancing, 
tokenization, stemming, etc. 
4- Model Selection: Five different algorithms are 
evaluated for sentiment analysis: a Multinomial 
Naïve Bayes (MNB) and Logistic regression (LR) 
classifiers as machine learning models, and also 
Recurrent Neural Network (RNN), Gated 
Recurrent Unit (GRU), and Long Short Term 
Memory (LSTM) as deep learning models. All the 
utilized deep learning models are implemented by 
using the Keras library on top of the TensorFlow. 
5- Model Training and Evaluation: The data is split 
into a training set and a testing set. The training set 
is used to train the models, and the testing set is 
used to evaluate their performance. Evaluation 
metrics, including precision, recall, and F1 measure 
score, are used to measure the accuracy of the 
models. 
6- Model Comparison: Comparison between the 
results of the models based on both accuracy and 
time performance. 
 
 
Figure 2: The proposed system steps 
 
4   Experimental and results 
In the following subsections, the experiments and results 
have been shown. An experimental setup is introduced in 
subsection 4.1, and the dataset description in detail is 
demonstrated in subsection 4.2. Lastly, the results and 
analysis of all the classifiers are presented in subsection 
4.3. 
 
4.1 Experimental setup 
The experiments were applied in the Windows 64-bits 
operating system. Additionally, anaconda with python 
3.8.8 has been used. Also, to implement deep learning 
algorithms TensorFlow and Keras frameworks have been 
installed. The rest environmental information’s shown in 
table 1. 
 
Table 1. The environment of tests. 
 
 
No. Used resource Resource information 
1 Operating System Windows 10, 64-bits 
2 Computer CPU 
Intel(R) Core (TM) i7-
4600U CPU @ 2.10 GHz 
2.70 GHz 
3 
Type of hard disk 
drive 
SSD 
4 Tensor Flow Version: 2.3.0 
5 Keras Version: 2.4.0 
6 Pandas: 1.2.4 
7 python Version 
The latest release of 
anaconda with python 3.8.8 

156 Informatica 47 (2023) 153–158 H.O. Ahmad et al. 
4.2   Dataset 
The dataset in the experiments is based on the 
different social media platforms. Many texts are not 
appropriate for the experiments, such as being either too 
long, having complex emotional tendencies, or having too 
many special characters. Hence, the original textual data 
are preprocessed. The dataset was taken from various 
social media site API which presents the financial 
comments. It consists of two attributes the first one is the 
comments and the second one is the label. Lastly, there are 
about 70000 records that meet the criteria of the 
algorithms which consist of positive, negative, and neutral 
data. The dichotomy of sentiment research in this paper is, 
negative which is represented by 0, neutral by 1 and, 
positive by 2. However, the selected 80% of the dataset is 
the training set, and 20% is the test set. Figure 3 shows the 
screenshot of the data before and after preprocessing.  
 
 
Figure 3: Snapshot of data before & after preprocessing. 
 
4.3   Results and analysis 
As mentioned before, the proposed work is consisting 
of two machine learning algorithms and three types of 
deep learning algorithms. Since the data is not balanced 
we did an up-sampling process on the minority classes and 
then concatenate the up-sampled data frames with the 
higher-class data frame. For the MNB we create a grid 
search object using the classifier and the hyperparameter 
dictionary and sets the number of folds for cross-
validation to 5. Also, for LR the maximum iteration was 
set at 500. On the other hand, the deep learning algorithms 
are applied in 10 epochs with 128 batch sizes. Finally, for 
each algorithm, the measures of accuracy, precision, 
recall, F1 score, and execution time performance have 
been computed as shown in the following table 2 and 
figures 4, and 5. 
 
Table 2. Results of the algorithms 
Algo. Preci. Recall F1-
score 
Acc. Exe. 
Time in 
Min. 
MNB 0.75 0.77 0.76 0.74 1.2 
LR 0.88 0.84 0.86 0.85 1.3 
RNN 0.96 0.92 0.94 0.94 12.6 
LSTM 0.96 0.96 0.96 0.96 121.1 
GRU 0.96 0.97 0.96 0.95 93.9 
 
 
Figure 4: Accuracy results of the algorithms. 
 
 
Figure 5: Execution time results of the algorithms. 
 
The MNB classifier achieved an accuracy of 74% and 
LR achieved 85% while the RNN, GRU, and LSTM 
models achieved accuracies of 94.2%, 95.8%, and 96.6%, 
respectively. These results suggest that the deep learning 
models outperformed the machine learning classifiers but 
their time performance relatively are high. In addition, the 
LSTM model achieves the highest accuracy with the 
highest execution time, on the other hand, the MNB 
classifier scored the lowest accuracy rate with the lowest 
execution time compared with the other algorithms. 
 
5   Discussion 
The results of this study demonstrate the potential of 
machine learning and deep learning algorithms for 
sentiment analysis of financial data. The deep learning 
models, in particular, showed significantly higher 
accuracy compared to the MNB and LR classifiers. This 
suggests that the use of more advanced algorithms can 
improve the performance of sentiment analysis for 
financial data. However, it is important to note that the 
accuracy of the models may be affected by various factors, 
such as the quality and quantity of the data, the 
preprocessing steps taken, and the specific parameters of 
the algorithms.  
Further optimization of the models, using techniques 
such as swarm optimization or bat optimization, may be 
necessary to achieve even higher accuracy in the future 
[25]. 
60
65
70
75
80
85
90
95
100
MNB LR RNN LSTM GRU
74
85
94,2
96,6
95,8
Acc.
0
15
30
45
60
75
90
105
120
135
MNB LR RNN LSTM GRU
1,2 1,3
12,6
121,1
93,9
Exe. time in Minutes 

Sentiment Analysis of Financial Textual data Using Machine Learning… Informatica 47 (2023) 153–158 157 
6   Conclusion 
This study collects and aggregates a financial dataset from 
multiple social media sources for sentiment analysis to 
inspect how the utilized preprocessing and the models are 
performed with the heterogeneous data. Python algorithm 
has been utilized to detect and collect the social comments 
in English language only. The experiments are classified 
the financial data into three polarities (positive, negative, 
and neutral) by using machine and deep learning 
algorithms. The paper made a comparison between five 
types of algorithms to find out which one provides more 
accuracy and less time execution. The models of deep 
learning are constructed by using the Keras package on top 
of TensorFlow. The dataset went through many 
preprocessing steps to increase the accuracy of the models 
such as feature selection, data cleaning, data balancing and 
etc. The process of data resampling and data balancing has 
made a significant impact on the accuracy of all the 
utilized algorithms. The scored accuracy results for all the 
constructed models in order are MNB 74%, LR 85% RNN 
94%, GRU 95%, and, LSTM 96%.  
The future work will focus on gaining better accuracy 
results by applying some optimization algorithms such as 
Swarm, Bat, etc. 
 
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