# HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting
Model

Ishu Gupta*
Cloud Computing Research Center
Department of Computer Science and Engineering
National Sun Yat-sen University
Kaohsiung, Taiwan
<EMAIL_ADDRESS>
Tarun Kumar Madan
Department of Computer Applications
National Institute of Technology
Kurukshetra, India
136119
<EMAIL_ADDRESS>Sukhman Singh
Department of Computer Applications
National Institute of Technology
Kurukshetra, India
136119
<EMAIL_ADDRESS>Ashutosh Kumar Singh
Department of Computer Applications
National Institute of Technology
Kurukshetra, India
136119
<EMAIL_ADDRESS>

###### Abstract

One of the pillars to build a country’s economy is the stock market. Over the
years, people are investing in stock markets to earn as much profit as
possible from the amount of money that they possess. Hence, it is vital to
have a prediction model which can accurately predict future stock prices. With
the help of machine learning, it is not an impossible task as the various
machine learning techniques if modeled properly may be able to provide the
best prediction values. This would enable the investors to decide whether to
buy, sell or hold the share. The aim of this paper is to predict the future of
the financial stocks of a company with improved accuracy. In this paper, we
have proposed the use of historical as well as sentiment data to efficiently
predict stock prices by applying LSTM. It has been found by analyzing the
existing research in the area of sentiment analysis that there is a strong
correlation between the movement of stock prices and the publication of news
articles. Therefore, in this paper, we have integrated these factors to
predict the stock prices more accurately.

_Keywords_ Stock market prediction $\cdot$ Machine learning $\cdot$ Long Short
Term Memory(LSTM) $\cdot$ Sentiment analysis $\cdot$ Historical analysis
$\cdot$ Tweepy $\cdot$ Twitter $\cdot$ TextBlob $\cdot$ National Stock
Exchange (NSE)

##  1 Introduction

The stock market of a country is one of the pillars of its economy [1, 2]. How
the stock market performs over the years paves the way for how the economy of
a country would grow or fall [3, 4, 5]. Since the financial markets are too
uncertain, it remains unclear that the investments would bear some profits or
incur some huge losses to the investors [6, 7, 8]. As a part of the economic
liberalization, the stock markets plays the most important role in the
financial schemes of the global corporate sector [9, 10, 11, 12]. The biggest
question for investors is what needs to be done with respect to a particular
stock, i.e. whether to purchase, sell or hold the shares for a stock [13, 14,
15, 16]. If the investors are able to invest in the right stocks, they will
bear good profits else they may lose their investments which would be a
setback for them as well as their country [17, 18, 19]. Hence, there’s a need
to devise such prediction models which may be helpful in predicting the stock
prices more accurately and more efficiently [20, 21, 22].

For stock market predictions various machine learning techniques have been
used to devise prediction models, few of these techniques are SVM (Support
Vector Machine) [23, 24], Linear Regression [25, 26], MLP [27, 28], LSTM (Long
Short Term Memory) [29], Random Forest [30, 31], ANN (Artificial Neural
Network) [32, 33], etc. The various factors that affect stock prices have been
used to develop these prediction models [34, 35, 36, 37]. The most predominant
one out of all the various factors is the prediction using historical data
[38, 39, 40]. The historical data signified the trend of the rate that had
been changing over time [41, 42, 43, 44]. With the course of time, it is
observed that historical data only isn’t sufficient to get the better accuracy
of the models [45, 46, 47]. In addition to historical data, sentiment data
(financial news, user tweets) must also be considered as an additional factor
for enhanced efficiency of the model. It is due to the reason that the
sentiment data considers the current scenarios like any natural calamity, new
policy change by the government, new foreign investments, etc. All these
factors affect the stock market significantly but are not reflected in the
historical data [48, 49, 50, 51], thus there’s a need to use the sentiment
data as well. Even though the inclusion of sentiment data in prediction models
isn’t easy but the efforts have been made by measuring these to integrate the
same for developing more accurate prediction models.

In this regard we have proposed an efficient Historical and Sentiment Analysis
based Stock Market Forecasting Model (HiSA-SMFM). The model utilizes
historical as well as sentiment data for more accurate stock market prediction
which majorly concerns the Indian Stock Market. The historical data has been
gathered from NSE India (National Stock Exchange) and the sentiment data has
been acquired using a Twitter API, Tweepy. LSTM machine learning technique is
used to integrate both these aspects of stock market. The key contributions of
this paper are as follows:

  * •

An efficient model is developed that employs both historical as well as
sentiment data for efficient stock market forecasting.

  * •

The tweets from Twitter through Tweepy API are extracted and then processed
using Text Blob for sentiment analysis.

  * •

The important features are extracted from historical and sentiment data,
integrated and trained by applying LSTM machine learning algorithm.

  * •

The experiments are conducted using ’Tata Motors Stock’ to validate the model
and compared with the state-of-the artwork to prove the better performance of
the model.

The rest of the paper is organized as follows: Section 2 discusses the key
contributions and research gap in the area. Section 3 describes the proposed
methodology including the technology involved and its working. The performance
of the proposed methodology is validated in section 4 that entails the
experimental set-up, benchmark datasets, obtained outcome, and the comparison
of the model. Finally, the conclusions and the future scope of the work is
presented in section 5.

##  2 Related Work

From the literature survey, it was observed that the machine learning
techniques for stock market prediction are being widely used thoroughly
throughout the world. Significant work has been done throughout the world in
prediction analysis. Many models of prediction have been proposed to date for
forecasting the stock prices and stock market trends [52, 53, 54].

Yan et al. [23] portrayed that the public mood and the stock market prices
have some relation between them. They tried to devise a relation between the
Chinese Stock Market and the Chinese local Microblogs. They worked on SVM and
Probabilistic Neural Network to make predictions. The experiments results
presented that Support Vector Machine provided better accuracy than the
Probabilistic Neural Network. It was observed that after using the public mood
as a feature, the accuracy increased by 20% compared to using historical data
only. Batra et al. [55] proposed a different way of using the SVM algorithm.
They extracted the sentiment from Twitter using StockTwits via an API of
python. The data extracted using the former was pre-processed for sentiment
analysis and Natural Language Processing (NLP). SVM was used to predict the
sentiment of each data and to classify the tweets into positive and negative
tweets respectively. The obtained output was combined with the available
historical data and used for the stock price prediction. It was observed that
the accuracy was 76.65% in this model.

Shi et al. [27] used Multi-Layer Perceptron to build a prediction model for
the stock market in China. They found that the sentiments play a more
significant role in prediction than the historical data. They gathered their
dataset using a Chinese social networking site - Xueqiu. Where, they extracted
the news feeds and user posts related to stocks. Their research work was based
on 3 features namely sentiment feature, stock-specific features (SSF), and
stock relatedness feature. The MLP technique was compared to SVM and it was
observed that MLP using stock-specific features and sentiment data proved to
be better than the same being used in association with SVM.

Pai et al. [28] in their research work divided their project into two phases,
the first one was related to predicting the stock prices on a daily basis, and
the other one was based on predicting the stock prices on a monthly basis.
Data for the experiments was extracted from Yahoo Finance, through which the
closing and opening prices of stocks were considered. The built model
considered both sentiment data and historical data. It was observed that they
achieved 70% accuracy. It was also observed that on the considered dataset,
Decision Boosted Tree gave more accurate results as compared to SVM and Linear
Regression.

Gao et al. [29] proposed LSTM. In their model, there were three layers in the
neural network including the input layer, the output layer, and the hidden
layer, and every unit in a layer was connected with all the units in adjacent
layers. It was observed that the MAPE of LSTM was the least equal to 0.7240.
Nelson et al. [56] also used LSTM to build their prediction model. They used
historical data to predict the stock market price movement. Initially in their
model, the acquired data was preprocessed and then later used for experiments.
It was found that the use of LSTM provided an average accuracy of around
55.9%. Sanboon et al. [1] compared the results of LSTM with support vector
machine, multilayer perceptron, decision tree, random forest, logistic
regression, and k-nearest neighbors. They used high, low, opening, and closing
prices as features of the input. According to the results of the algorithm,
the buy and sell recommendations were given. LSTM gave more accurate results
compared to the other algorithms.

Although, a number of models have been proposed, few of these techniques used
historical data while other focused on sentiment analysis for prediction,
still an approach can be developed to enhance the model performance since
these dataset alone are not sufficient. The major drawback of the existing
work is that the models used a predefined values of sentiments and which is
not feasible and efficient in reality. In order to overcome this drawback, a
model should be developed in which this factor can be updated dynamically as
per feasibility. Further, in the models, a single feature of the historical
data is considered. A model is need to be developed that can accommodate
multi-features. In this regard, we propose an approach named HiSA-SMFM for
enhanced stock market forecasting based on historical and sentiment data
analysis by applying LSTM machine learning algorithm. HiSA-SMFM takes into
account multi-features namely open price (historical), positive reviews
(sentiment), and negative reviews (sentiment).

##  3 Proposed HiSA-SMFM

In this section, the various technologies used and working of the proposed
model are described.

###  3.1 Technologies

####  3.1.1 Long Short Term Memory (LSTM)

Long Short Term Memory (LSTM) [57] is a machine learning technique that was
developed by Hochreiter and Schmidhuber in 1997. It was introduced to overcome
the flaws of its predecessor RNN (Recurrent Neural Network). The biggest
drawback of RNN is that it lacks learning property when the extent in between
the requirement and previous information increases, this is known as the long-
term dependency problem. LSTM overcomes the long-term dependency problem as it
has the ability to hold the values for both long and short duration of time.

LSTM has a totally different architecture as compared to other neural network
models. Considering RNN, it has a very simple feedback loop neural network
design whereas LSTM consists of a memory block or a cell positioned inside a
single neural network layer. Since LSTM are good at remembering information
for quite a long time, it becomes the first choice for use as it enhances the
accuracy of the prediction models.

In our work, we proposed a model that uses an LSTM network for prediction. Our
model incorporates historical as well as sentiment data to predict future
stock prices. Fig. 1 shows how the LSTM Network works, here $C_{t}-1$ is the
old cell state, $C_{t}$ is present cell state, $h_{t}-1$ is an output of the
previous cell, $h_{t}$ is an output of the present cell, it is input gate
layer, $f_{t}$ is forget gate layer and it is output sigmoid gate layer.

Figure 1: LSTM cell

####  3.1.2 Twitter API – Tweepy

Twitter, one of the most popular microblogging websites plays a vital role in
sentiment analysis for various fields like election results prediction [58],
cryptocurrency price prediction [59], etc. Twitter provides the feature to
mine their tweets data for research purposes using a Twitter API or Tweepy.
The user needs to register first as a developer to gain access, then a set of
keys are generated including consumer key, consumer secret, access key, and
access secret. These keys are essential to link the code with the Twitter
server and fetch the required data as legitimate users.

####  3.1.3 TextBlob

TextBlob is used to combine the two different kinds of judgments together,
distinguish between the positive news and the negative news, and then further
quantify the result of the sentiment analysis [60]. It is an open-source text
processing library written in Python, which provides an API for common NLP
tasks such as part-of-speech tagging, noun phrase extraction, sentiment
analysis, etc. Each word in the TextBlob lexicon contains the value of
polarity, subjectivity and intensity. The value of polarity varies from -1 to
1. Fig. 2 portrays the working of sentiment analysis where classification of
text is done in three classes positive, neutral, and negative.

Figure 2: Working of sentiment analysis

###  3.2 Working

The proposed model extracts the features for training the prediction model by
analyzing both historical as well as sentiment data. The tweets from Twitter
are extracted through Tweepy API and then processed for sentiment analysis
using Text Blob. After this, we extract the historical data from NSE India.
Then a model is trained for stock market prediction using stock price data and
sentiment score to predict the change in the stock market. Further, the stock
price prediction is performed using LSTM by adding new features into the
historical dataset. The features added in the dataset are the sentiment data
classified into positive and negative classes and their respective
percentages.

The proposed methodology for predicting the stock market movement through
sentiment analysis is carried out in 5 phases as shown in Fig. 3 that
describes the working of the proposed model. The 1st phase gathers the data
for historical and sentiment analysis from NSE India and Twitter respectively.
In the 2nd phase, the historical data is pre-processed. The data obtained from
NSE and Twitter can’t be used in the prediction model as it is, hence few
amendments are essential to incorporate the data. The historical data needs to
be normalized into a certain range which would maintain the trend of the
market as there may be instances where the data outlies a certain boundary and
might totally change the outcome. Also, the data may contain null or missing
values, therefore it is important to rectify them by using the mean of the
data so the trend is not hampered. Regarding the sentiment data, it is
essential to use the percentage values of the obtained tweets. Once the data
is classified using TextBlob, the percentage of each class is computed and
added to the data. The 3rd phase classifies the data collected from Twitter
using Tweepy into 3 classes, positive, negative, and neutral using TextBlob.
In the 4th phase, both sentiment data and historical data are merged into one
common dataset for better calculations and enhancing efficiency. The 5th phase
predicts the output (stock prices) with the help of the LSTM network.

Figure 3: Working of HiSA-SMFM

##  4 Performance Evaluation

###  4.1 Experimental Set-up and Benchmark Dataset

The experiments for stock price prediction are conducted using ’Tata Motors
dataset’ to analyze the efficiency of the proposed HiSA-SMFM. In HiSA-SMFM,
three features namely open price (historical), positive reviews (sentiment),
and negative reviews (sentiment) from the integrated sentiment and historical
data are take into consideration for training the model. Furthermore, the
outputs are compared with Deep Learning Prediction Model (DLPM) presented in
[1] for validating the performance of the model

####  4.1.1 Historical data

The historical data from Tata Motors in the Indian Stock Market is obtained
from NSE (National Stock Exchange) India website. The data set contains data
for 1 year out of which data for 9 months has been used as a training dataset
and the remaining data of 3 months is used as the test dataset. The dataset
contains various features like date, volume, closing price, opening price,
highest price, lowest price, and adjusted closing price.

####  4.1.2 Sentiment data

For the sentiment analysis part, the sentiment data has been fetched from a
world-renowned microblogging website, Twitter. Using one of the Twitter API
known as Tweepy, tweets can be fetched by providing certain keywords. The API
searches the entire database that Twitter possesses and returns the tweets
related to that keyword. Once the data has been collected, a python library
known as TextBlob is used to segregate the data into three classes positive,
negative and neutral. The output of Textblob is stored in the form of
percentages for the various classes.

###  4.2 Experimental Results

The experiments are conducted on a different epoch sizes to measure the
throughout performance of the model.

####  4.2.1 Real Vs Predicted Forecasting

Figs. 4 and 5 show the real vs predicted price with epoch size 5 of comparable
DLPM [1] and proposed HiSA-SMFM individually. It is found that HiSA-SMFM
predicts the stock market near to the real value which is far better than
prediction by [1] since the training is performed by extracting multi-features
from both historical as well as sentiment data in HiSA-SMFM .

Figure 4: Real vs predicted price with epoch size 5 of DLPM [1] Figure 5: Real
vs predicted price with epoch size 5 of HiSA-SMFM

Figs. 6 and 7 depict the real vs predicted price with epoch size 10 of
comparable DLPM [1] and proposed HiSA-SMFM respectively. It is observed that
the prediction of HiSA-SMFM is nearly same as the real value which is better
than DLPM [1] since the integrated data of sentiment and historical is taken
into account in HiSA-SMFM while historical data only is used in [1].

Figure 6: Real vs predicted price with epoch size 10 of DLPM [1] Figure 7:
Real vs predicted price with epoch size 10 of HiSA-SMFM

Figs. 8 and 9 portray the real vs predicted price with epoch size 15 of
comparable DLPM [1] and proposed HiSA-SMFM relatively. It is observed that the
prediction of HiSA-SMFM is much better than DLPM [1] where the real and
predicted values are far away with each other. It is due to the reason of
using both historical as well as sentiment data for prediction in proposed
HiSA-SMFM that extract the more accurate pattern from the input dataset.

Figure 8: Real vs predicted price with epoch size 15 of DLPM [1] Figure 9:
Real vs predicted price with epoch size 15 of HiSA-SMFM

####  4.2.2 Accuracy

The accuracy of comparable DLPM [1] and proposed HiSA-SMFM is shown in Table 1
at epoch size 5, 10, 15 followed by evaluation of average accuracy of both the
models. The accuracy are 91.58%, 95.41% at epoch size 5, 94.56% and 97.18% at
epoch size 10 while 83.46% and 92.38% at epoch size 15 respectively for DLPM
[1] and proposed HiSA-SMFM. The proposed model is found to be better than DLPM
[1]. It is found that the trend remains the same even for different epoch
sizes. HiSA-SMFM ensures a significant gain of 3.82%, 2.62%, and 8.92% over
DLPM [1] for epoch sizes 5, 10, and 15 respectively. The results show that
HiSA-SMFM attains a significant improvements compared to DLPM [1]. Further,
DLPM [1] obtains an average accuracy of 89.87% while it is 94.99% for HiSA-
SMFM. A gain of more than 5% is observed by the proposed model which proved
HiSA-SMFM is better than state-of-the artwork DLPM [1]. HiSA-SMFM ensures
better performance over [1] since it considers three features namely open
price (historical), positive reviews (sentiment), and negative reviews
(sentiment) from integrated historical and sentiment data for training the
model that helps in extracting the better pattern.

Table 1: Comparison of Accuracy at Various Epochs Epoch size | Model | Accuracy
---|---|---
5 | DLPM [1] | 91.59%
| HiSA-SMFM | 95.41%
10 | DLPM [1] | 94.56%
| HiSA-SMFM | 97.18%
15 | DLPM [1] | 83.46%
| HiSA-SMFM | 92.38%
Average | DLPM [1] | 89.87%
| HiSA-SMFM | 94.99%

##  5 Conclusions and Future Directions

As the stock market is too uncertain, the investors must invest their money
after assessing the affecting factors such as public reviews, historical data,
and news events from social media websites. Many researchers have tried to
devise prediction models using machine learning algorithms to predict the
accurate prices of stocks using various tools and techniques, but have not
been able to come up with the best possible solution. In this regard, this
paper proposes a novel and efficient stock market forecasting solution. The
proposed model utilized the LSTM algorithm to predict the future trend of the
stock market. We analyzed and combined the features of historical and
sentiment data and used the LSTM algorithm to predict the future trend. We
used the opening price as a prediction feature for the historical data, and
Tweepy to classify the tweets acquired from Twitter. We evaluated the
performance of proposed model at different epoch sizes and compared it with
state-of-the artwork. It is found that the average accuracy of the proposed
model is increased by more than 5% over the existing work which is a
significant improvement.

The crucial challenge faced while conducting the research work was the
extraction of the sentiment data or the financial news related to certain
stocks from social media websites such as Twitter followed by pre-processing
of it as per the model. There are chances that people may even post fake
good/bad reviews related to a particular stock so that they can either uplift
or downgrade their reputation. In the future, the hybrid/fusion models can be
developed to overcome the above-mentioned challenge as these models can use
different techniques to acquire and process sentiment data, news events, and
historical data. Also, these models have the potential to use the capabilities
of their individual components that will help in forecasting more accurate
results. Apart from these, a company’s balance sheet or the cash flow can be
considered as an important factors to forecast the stocks.

## References

  * [1] T. Sanboon, K. Keatruangkamala, and S. Jaiyen, “A Deep Learning Model for Predicting Buy and Sell Recommendations in Stock Exchange of Thailand using Long Short-Term Memory,” in _2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS)_ , 2019, pp. 757–760.
  * [2] I. Gupta and A. K. Singh, “A Probability based Model for Data Leakage Detection using Bigraph,” in _Proceedings of 7th International Conference on Communication and Network Security (ICCNS)_ , ser. ICCNS 2017\. New York, NY, USA: Association for Computing Machinery (ACM), 2017, p. 1–5.
  * [3] I. Gupta, R. Gupta, A. K. Singh, and R. Buyya, “MLPAM: A Machine Learning and Probabilistic Analysis Based Model for Preserving Security and Privacy in Cloud Environment,” _IEEE Systems Journal_ , vol. 15, no. 3, pp. 4248–4259, 2021.
  * [4] D. Saxena, I. Gupta, J. Kumar, A. K. Singh, and X. Wen, “A Secure and Multiobjective Virtual Machine Placement Framework for Cloud Data Center,” _IEEE Systems Journal_ , pp. 1–12, 2021.
  * [5] U. Arora, S. Verma, I. Gupta, and A. K. Singh, “Implementing Privacy using Modified Tree and Map Technique,” in _2017 3rd International Conference on Advances in Computing,Communication & Automation (ICACCA) (Fall)_. IEEE, 2017, pp. 1–5.
  * [6] A. K. Singh and I. Gupta, “Online Information Leaker Identification Scheme for Secure Data Sharing,” _Multimedia Tools and Applications_ , vol. 79, no. 41, pp. 31 165–31 182, November 2020.
  * [7] K. Kaur, I. Gupta, and A. K. Singh, “A Comparative Study of the Approach Provided for Preventing the Data Leakage,” _International Journal of Network Security & Its Applications_, vol. 9, no. 5, pp. 21–33, 2017.
  * [8] I. Gupta and A. K. Singh, “SELI: Statistical Evaluation based Leaker Identification Stochastic Scheme for Secure Data Sharing,” _IET Communications_ , vol. 14, pp. 3607–3618, December 2020.
  * [9] A. Kesharwani, A. Nag, A. Tiwari, I. Gupta, B. Sharma, and A. K. Singh, “Real-Time Human Locator and Advance Home Security Appliances,” in _Evolutionary Computing and Mobile Sustainable Networks_ , vol. 53. Singapore: Springer Singapore, 2021, pp. 37–49, data Engineering and Communications Technologies.
  * [10] I. Gupta and A. K. Singh, “GUIM-SMD: Guilty User Identification Model using Summation Matrix-based Distribution,” _IET Information Security_ , vol. 14, pp. 773–782, November 2020.
  * [11] Khushbu, P. Nishad, V. Kashyap, I. Gupta, and A. K. Singh, “An Organized Study on Data Divulge Elimination and Discernment,” in _Computer Networks and Inventive Communication Technologies_. Singapore: Springer Singapore, 2021, pp. 569–578.
  * [12] I. Gupta and A. K. Singh, “Dynamic Threshold based Information Leaker Identification Scheme,” _Information Processing Letters_ , vol. 147, pp. 69 – 73, 2019.
  * [13] S. Jalwa, V. Sharma, A. R. Siddiqi, I. Gupta, and A. K. Singh, “Comprehensive and Comparative Analysis of Different Files Using CP-ABE,” in _Advances in Communication and Computational Technology_ , vol. 668. Singapore: Springer Singapore, 2021, pp. 189–198, electrical Engineering.
  * [14] I. Gupta and A. K. Singh, “An Integrated Approach for Data Leaker Detection in Cloud Environment,” _Journal of Information Science and Engineering_ , vol. 36, pp. 993–1005, Sep. 2020.
  * [15] R. Verma, V. Gautam, C. P. Yadav, I. Gupta, and A. K. Singh, “A Survey on Data Leakage Detection and Prevention,” in _Proceedings of the International Conference on Innovative Computing & Communications (ICICC) 2020_. SSRN, Elsevier, May 2020, pp. 1–7.
  * [16] I. Gupta, N. Singh, and A. Singh, “Layer-based Privacy and Security Architecture for Cloud Data Sharing,” _Journal of Communications Software and Systems (JCOMSS)_ , vol. 15, no. 2, 2019.
  * [17] I. Gupta and A. K. Singh, “A Framework for Malicious Agent Detection in Cloud Computing Environment,” _International Journal of Advanced Science and Technology (IJAST)_ , vol. 135, pp. 49–62, Feb 2020.
  * [18] A. Acharya, H. Prasad, V. Kumar, I. Gupta, and A. K. Singh, “Host Platform Security and Mobile Agent Classification: A Systematic Study,” in _Computer Networks and Inventive Communication Technologies_ , vol. 58. Singapore: Springer Singapore, 2021, pp. 1001–1010, data Engineering and Communications Technologies.
  * [19] I. Gupta and A. K. Singh, “A Probabilistic Approach for Guilty Agent Detection using Bigraph after Distribution of Sample Data,” _Procedia Computer Science_ , vol. 125, pp. 662 – 668, 2018.
  * [20] K. Kaur, I. Gupta, and A. K. Singh, “Data Leakage Prevention: E-Mail Protection via Gateway,” _Journal of Physics: Conference Series_ , vol. 933, p. 012013, jan 2018.
  * [21] P. Godha, S. Jadon, A. Patle, I. Gupta, B. Sharma, and A. Kumar Singh, “Architecture, an Efficient Routing, Applications, and Challenges in Delay Tolerant Network,” in _2019 International Conference on Intelligent Computing and Control Systems (ICCS)_. IEEE, 2019, pp. 824–829.
  * [22] I. Gupta, S. Mittal, A. Tiwari, P. Agarwal, and A. K. Singh, “TIDF-DLPM: Term and Inverse Document Frequency based Data Leakage Prevention Model,” 2022.
  * [23] D. Yan, G. Zhou, X. Zhao, Y. Tian, and F. Yang, “Predicting Stock Using Microblog Moods,” _China Communications_ , vol. 13, no. 8, pp. 244–257, 2016.
  * [24] S. Shen, H. Jiang, and T. Zhang, “Stock Market Forecasting Using Machine Learning Algorithms,” 2012, pp. 1–5. [Online]. Available: http://cs229.stanford.edu/proj2012/ShenJiangZhang-StockMarketForecastingusingMachineLearningAlgorithms.pdf
  * [25] M. A. Shah and C. D. Bhavsar, “Predicting Stock Market using Regression Technique ,” _Research Journal of Finance and Accounting_ , vol. 6, no. 3, pp. 27–34, 2015.
  * [26] S. S. Roy, D. Mittal, A. Basu, and A. Abraham, “Stock Market Forecasting Using LASSO Linear Regression Model,” in _Afro-European Conference for Industrial Advancement_ , vol. 334. Cham: Springer International Publishing, 2015, pp. 371–381, advances in Intelligent Systems and Computing.
  * [27] X. Zhang, J. Shi, D. Wang, and B. Fang, “Exploiting Investors Social Network for Stock Prediction in China’s Market,” _Journal of Computational Science_ , vol. 28, 11 2017.
  * [28] V. Turchenko, P. Beraldi, F. De Simone, and L. Grandinetti, “Short-term Stock Price Prediction Using MLP in Moving Simulation Mode,” in _Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems_ , vol. 2, 2011, pp. 666–671.
  * [29] T. Gao, Y. Chai, and Y. Liu, “Applying Long Short Term Momory Neural Networks for Predicting Stock Closing Price,” in _8th IEEE International Conference on Software Engineering and Service Science_ , 2017, pp. 575–578.
  * [30] M. Ballings, D. Van den Poel, N. Hespeels, and R. Gryp, “Evaluating Multiple Classifiers for Stock Price Direction Prediction,” _Expert Systems with Applications_ , vol. 42, no. 20, pp. 7046–7056, 2015.
  * [31] T. Manojlovic and I. Štajduhar, “Predicting Stock Market Trends Using Random Forests: A Sample of The Zagreb Stock Exchange,” in _2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)_ , Opatija, 05 2015, pp. 1189–1193.
  * [32] G. G. Rajput and B. H. Kaulwar, “A Comparative Study of Artificial Neural Networks and Support Vector Machines for Predicting Stock Prices in National Stock Exchange of India,” in _2019 International Conference on Data Science and Communication (IconDSC)_ , 2019, pp. 1–7.
  * [33] A. H. Moghaddam, M. H. Moghaddam, and M. Esfandyari, “Stock Market Index Prediction Using Artificial Neural Network,” _Journal of Economics, Finance and Administrative Science_ , vol. 21, no. 41, pp. 89–93, 2016.
  * [34] P. Tiwari, S. Mehta, N. Sakhuja, I. Gupta, and A. K. Singh, “Hybrid Method in Identifying the Fraud Detection in the Credit Card,” in _Evolutionary Computing and Mobile Sustainable Networks_ , vol. 53. Singapore: Springer Singapore, 2021, pp. 27–35, data Engineering and Communications Technologies.
  * [35] A. Nag, A. Kesharwani, B. Sharma, I. Gupta, A. Tiwari, and A. K. Singh, “Potential and Extention of Internet of Things,” in _Second International Conference on Computer Networks and Communication Technologies (ICCNCT)_ , vol. 44. Cham: Springer International Publishing, 2020, pp. 542–551.
  * [36] Khushbu, P. Nishad, V. Kashyap, and I. Gupta, “A Classification and Distribution Model for Data Leakage Prevention and Detection,” _International Research Journal of Modernization in Engineering Technology and Science_ , vol. 3, no. 2, pp. 348–354, Feb. 2021.
  * [37] A. K. Singh, I. Gupta, R. Verma, V. Gautam, and C. P. Yadav, “A Survey on Data Leakage Detection and Prevention,” in _Proc. Int. Conf. Innov. Comput. Commun._ , 2020.
  * [38] P. Godha, S. Jadon, A. Patle, I. Gupta, B. Sharma, and A. K. Singh, “Flooding and Forwarding Based on Efficient Routing Protocol,” in _International Conference on Innovative Computing and Communications_ , vol. 1166. Singapore: Springer Singapore, 2021, pp. 215–223, advances in Intelligent Systems and Computing.
  * [39] I. Gupta and A. K. Singh, “A Holistic View on Data Protection for Sharing, Communicating, and Computing Environments: Taxonomy and Future Directions,” 2022\.
  * [40] I. Gupta and K. Gupta, “Evaluation of Intrusion Detection Schemes in Wireless Sensor Network,” _IOSR Journal of Computer Engineering_ , vol. 18, no. 2, pp. 60–63, Mar-Apr. 2016.
  * [41] V. Sharma, S. Jalwa, A. R. Siddiqi, I. Gupta, and A. K. Singh, “A Lightweight Effective Randomized Caesar Cipher Algorithm for Security of Data,” in _Evolutionary Computing and Mobile Sustainable Networks_ , vol. 53. Singapore: Springer Singapore, 2021, pp. 411–419, data Engineering and Communications Technologies.
  * [42] P. Agarwal, S. Mittal, A. Tiwari, I. Gupta, A. K. Singh, and B. Sharma, “Authenticating Cryptography over Network in Data,” in _2019 International Conference on Intelligent Computing and Control Systems (ICCS)_. IEEE, 2019, pp. 632–636.
  * [43] A. Acharya, H. Prasad, V. Kumar, I. Gupta, and A. K. Singh, “MACI: Malicious API Call Identifier Model to Secure the Host Platform,” in _Proceedings of the Seventh International Conference on Mathematics and Computing_. Singapore: Springer Singapore, 2022, pp. 309–320.
  * [44] I. Gupta and A. K. Singh, “A Confidentiality Preserving Data Leaker Detection Model for Secure Sharing of Cloud Data using Integrated Techniques,” in _2019 7th International Conference on Smart Computing Communications (ICSCC)_. Curtin University, Sarawak Malaysia: IEEE, 2019, pp. 1–5.
  * [45] K. N. Kaur, Divya, I. Gupta, and A. K. Singh, “Digital Image Watermarking Using (2, 2) Visual Cryptography with DWT-SVD Based Watermarking,” in _Computational Intelligence in Data Mining_ , vol. 711. Singapore: Springer Singapore, 2019, pp. 77–86, advances in Intelligent Systems and Computing.
  * [46] K. Kaur, I. Gupta, and A. K. Singh, “A Comparative Evaluation of Data Leakage/Loss Prevention Systems (DLPS),” in _Proc. 4th International Conference Computer Science & Information Technology_, 2017, pp. 87–95.
  * [47] G. Batra, H. Singh, I. Gupta, and A. K. Singh, “Best Fit Sharing and Power Aware (BFSPA) Algorithm for VM Placement in Cloud Environment,” in _2017 3rd International Conference on Advances in Computing,Communication & Automation (ICACCA) (Fall)_. IEEE, 2017, pp. 1–4.
  * [48] I. Gupta, V. Sharma, S. Kaur, and A. K. Singh1, “PCA-RF: An Efficient Parkinson’s Disease Prediction Model based on Random Forest Classification,” 2022.
  * [49] I. Gupta, “A Comparative Study of the Approach Provided for Preventing the Data Leakage,” _Other Topics Engineering Research eJournal_ , vol. 9, no. 5, September 2017.
  * [50] I. Gupta and K. Gupta, “Review on Intrusion Detection System Architectures in WSN,” _International Journal of Scientific & Engineering Research_, vol. 7, no. 12, pp. 111–115, Dec. 2016.
  * [51] K. Gupta and I. Gupta, “A Comprehensive Study on Architecture, Security issues and Challenges in Cloud Computing,” _International Journal of Scientific & Engineering Research_, vol. 7, no. 12, pp. 128–131, Dec. 2016.
  * [52] X. Zhang, S. Qu, J. Huang, B. Fang, and P. Yu, “Stock Market Prediction via Multi-Source Multiple Instance Learning,” _IEEE Access_ , vol. 6, pp. 50 720–50 728, 2018.
  * [53] L. Chen, Z. Qiao, M. Wang, C. Wang, R. Du, and H. E. Stanley, “Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market?” _IEEE Access_ , vol. 6, pp. 48 625–48 633, 2018.
  * [54] Y. Gupta and P. Kumar, “Real-Time Sentiment Analysis of Tweets: A Case Study of Punjab Elections,” in _IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT)_ , 2019, pp. 1–12.
  * [55] R. Batra and S. M. Daudpota, “Integrating Stocktwits with Sentiment Analysis for Better Prediction of Stock Price Movement,” in _2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)_ , Sukkur, 2018, pp. 1–5.
  * [56] A. C. M. P. D. M. Q. Nelson and R. A. de Oliveira, “Stock Market’s Price Movement Prediction with LSTM Neural Networks,” in _2017 International Joint Conference on Neural Networks (IJCNN)_ , Anchorage, AK, May 2017, pp. 1419–1426. [Online]. Available: https://doi.org/10.1109/IJCNN.2017.7966019
  * [57] H. M, G. E.A., V. K. Menon, and S. K.P., “NSE Stock Market Prediction Using Deep-Learning Models,” _Procedia Computer Science_ , vol. 132, pp. 1351–1362, 2018, international Conference on Computational Intelligence and Data Science. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1877050918307828
  * [58] O. Almatrafi, S. Parack, and B. Chavan, “Application of Location-Based Sentiment Analysis Using Twitter for Identifying Trends towards Indian General Elections 2014,” in _Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication_ , ser. IMCOM ’15. New York, NY, USA: Association for Computing Machinery, 2015. [Online]. Available: https://doi.org/10.1145/2701126.2701129
  * [59] J. Abraham, D. Higdon, J. Nelson, and J. Ibarra, “Cryptocurrency Price Prediction Using Tweet Volumes and Sentiment Analysis,” _SMU Data Science Review_ , vol. 1, no. 3, 2018.
  * [60] S. Ao, “Sentiment Analysis Based on Financial Tweets and Market Information,” in _2018 International Conference on Audio, Language and Image Processing (ICALIP)_ , 2018, pp. 321–326.