Hadoop Framework

Transformers owe their success to both using Feedforward Neural Networks and Scaled Dot Product Attention in one model to both represent and filter unwanted information. Most attention-based Deep Learning multimodal fusion models use Sigmoid or Softmax of layers to filter modalities that have less supervised learning power or contain a lot of noise or outliers. Inspired by the models aforementioned, we propose a modification to perceptron (one-hidden-layered Feedforward Neural Network (FFNN)) to take advantages from both representer (fully connected layer) and filterer (Sigmoid neuron weighter). To the best of author's knowledge, this is the first time attention weights are used at neuron level (instead of modality level, without weighted average, from a different layer rather than FFNN hidden layer) in all FFNN layers. We call the proposed architecture "Attentron" as a combination of neuron-wise attention and Perceptron. The performance analyses have been conducted on ConvNet's Dense layers and Transformer's FFNN modules. The results show improved accuracy over models (baseline) without Sigmoid attention mechanism.

Adverse conditions within specific offshore environments magnify the challenges faced by a vessel’s energy-efficiency optimization in the Industry 4.0 era. As the data rate and volume increase, the analysis of big data using analytical techniques might not be efficient, or might even be infeasible in some cases. The purpose of this study is the development of deep-learning models that can be utilized to predict the propulsion power of a vessel. Two models are discriminated: (1) a feed-forward neural network (FFNN) and (2) a recurrent neural network (RNN). Predictions provided by these models were compared with values measured onboard. Comparisons between the two types of networks were also performed. Emphasis was placed on the different data pre-processing phases, as well as on the optimal configuration decision process for each of the developed deep-learning models. Factors and parameters that played a significant role in the outcome, such as the number of layers in the neural netw...

Sentiment Analysis plays a vital role in Natural Language Processing (NLP) which aims to discern opinions and emotions expressed in text. However, the data sparsity and disambiguation of natural languages make it challenging for the existing approaches to provide accurate extraction and classification when subjected to text data. Hence, this research proposes Multihead Self-Attention Mechanism based Bidirectional Gated Recurrent Unit (MSA-BiGRU) approach for the classification of sentiment data into multi-classes. The MSA allows the BiGRU to consider various parts of the sequence, capturing long-term dependencies and relationships within the text. Initially, three standard datasets namely, Internet Movie Database (IMDB), Sentiment140 and World Cup Soccer are utilized to estimate the effectiveness of the MSA-BiGRU method. The Word-2-Vector (Word2Vec) is utilized for the feature extraction process and Analysis of Variance (ANOVA) is utilized for the selection of features. The performance metrices: accuracy, precision, recall and F1-score are utilized to validate the model's effectiveness. The experimental results show that the MSA-BiGRU method attains a better accuracy of 98.91% on sentiment140 dataset as compared to Automated Sentiment Analysis in social media based on Harris Hawks Optimization with Deep Learning (ASASM-HHODL) and Gated Attention Mechanism and Recurrent Neural Network (GARN).

COVID-19 pandemic has received a serious attention from academia, industry and governments to stop the huge number of deaths and economic disruptions around the world. Many techniques have been used to control the spread of the pandemic by understanding its characteristics and behavior. However, because of the large amounts and complex characteristics of COVID-19 data, the querying and analysis of such data using conventional tools have become a challenging task. As a result, powerful and distributed tools are highly required for querying and analyzing this data effectively. In this paper, distributed system using Hive based on Hadoop cluster is used to query and analyze COVID-19 data to obtain meaningful information. Hadoop is employed as a scalable and reliable framework to accommodate such large amounts of data. Hive is used as a data warehouse that run on Hadoop cluster to perform querying and predictive analytics on huge COVID-19 datasets. Several experiments are performed to evaluate the performance of proposed system. Experiments show that the proposed system outperforms relational database management system (RDBMS) in terms of query processing time. Experiments also show that the proposed system has a better efficiency in terms of data load, I/O operation, reading and writing data.

Real-time data processing has become increasingly important in today's data-driven world, where organizations need to quickly analyze and respond to incoming data to maintain a competitive edge. PySpark, an open-source distributed computing framework, has emerged as a powerful tool for processing large-scale data in real-time. This paper analyzes the capabilities of PySpark for real-time data processing, focusing on its scalability, flexibility, and integration with other big data technologies. PySpark provides a robust framework for handling real-time data streams, leveraging Apache Spark's distributed computing power and the ease of use of the Python programming language. The paper discusses the architecture of PySpark, including its core components and how it processes data in memory, which
significantly enhances performance over traditional disk-based systems.
The analysis explores PySpark's compatibility with various data sources, such as Kafka, HDFS, and Amazon
S3, enabling seamless data ingestion and processing. PySpark's ability to handle structured, semi-structured,
and unstructured data allows it to be used in diverse applications, from financial analytics to social media
monitoring and IoT device management.
Moreover, PySpark's support for machine learning libraries such as MLlib enables the development of realtime predictive models, enhancing its value in data-driven decision-making. This paper also examines
PySpark's scalability, demonstrating how it efficiently handles data growth and varying workload demands
by utilizing dynamic resource allocation and parallel processing.
In addition, the paper highlights PySpark's integration with other big data technologies, such as Apache Hive
and Apache HBase, which allows for enhanced data storage and querying capabilities. These integrations
enable PySpark to provide comprehensive solutions for real-time analytics, from data ingestion to
visualization.

The amount of data generated due to the development of IT technology is increasing exponentially every year. As an alternative to this, research on distributed systems and in-memory-based big data processing techniques is being actively conducted. The processing performance of existing big data processing techniques processes big data faster as the number of nodes and memory capacity increases. However, the increase in the number of nodes increases the frequency of failures in the big data infrastructure environment, and increases infrastructure management points and infrastructure operation costs. In addition, the increase in memory capacity increases the infrastructure cost for node configuration. Therefore, in this paper, we propose an in-memory-based hybrid big data processing technique to improve big data processing rate. The proposed method adds a combiner step to the distributed system processing method, and at that stage, by applying in-memory-based processing technology, the big data processing time is reduced by approximately compared to the existing distributed system-based big data processing method. In the future, for practical verification of the proposed technique, realistic performance evaluation in a big data infrastructure environment composed of more nodes is required.

Big Data (BD) is associated with a new generation of technologies and architectures which can harness the value of extremely large volumes of very varied data through real time processing and analysis. It involves changes in (1) data types, (2) accumulation speed, and (3) data volume. This paper presents the main concepts related to the BD paradigm, and introduces architectures and technologies for BD and BD sets. The integration of BD with the Hadoop Framework is also underlined. BD has attracted a lot of attention in the public sector due to the newly emerging technologies that allow the availability of network access. The volume of different types of data has exponentially increased. Some applications of BD in the public sector in Romania are briefly presented. Keywords—Big data, big data Analytics, Hadoop framework, cloud computing.

The purpose of this study is to study and forecast the crude oil prices in India using Autoregressive Integrated Moving Average (ARIMA) model of time series analysis. The report tables, charts, and ARIMA model are used to forecast the prices of crude oil in India. Also, in this study we have forecasted crude oil prices and BSE oil & Gas index accuracy of model is found to be 90% The forecast will help investors to take early bird decisions & help to make necessary strategies & planning of business.

Opinion Mining (OM) is a field of Natural Language Processing (NLP) that aims to capture human sentiment in the given text. With the ever-spreading of online purchasing websites, micro-blogging sites, and social media platforms, OM in online social media platforms has picked the interest of thousands of scientific researchers. Because the reviews, tweets and blogs acquired from these social media networks, act as a significant source for enhancing the decision making process. The obtained textual data (reviews, tweets, or blogs) are classified into three different class labels which are negative, neutral and positive for analyzing and extracting relevant information from the given dataset. In this contribution, we introduce an innovative MapReduce improved weighted ID3 decision tree classification approach for OM, which consists mainly of three aspects: Firstly We have used several feature extractors to efficiently detect and capture the relevant data from the given tweets, including N-grams or character-level, Bag-Of-Words, word embedding (GloVe, Word2Vec), FastText, and TF-IDF. Secondly, we have applied a multiple feature selector to reduce the high feature's dimensionality, including Chi-square, Gain Ratio, Information Gain, and Gini Index. Finally, we have employed the obtained features to carry out the classification task using an improved ID3 decision tree classifier, which aims to calculate the weighted information gain instead of information gain used in traditional ID3. In other words, to measure the weighted information gain for the current conditioned feature, we follow two steps: First, we compute the weighted correlation function of the current conditioned feature. Second, we multiply the obtained weighted correlation function by the information gain of this current conditioned feature. This work is implemented in a distributed environment using the Hadoop framework, with its programming framework MapReduce and its distributed file system HDFS. Its primary goal is to enhance the performance of a well-known ID3 classifier in terms of accuracy, execution time, and ability to handle the massive datasets. We have carried out several experiences that aims to assess the effectiveness of our suggested classifier compared to some other contributions chosen from the literature. The experimental results demonstrated that our ID3 classifier works better on COVID-19_Sentiments dataset than other classifiers in terms of Recall (85.72 %), specificity (86.51 %), error rate (11.18 %), false-positive rate (13.49 %), execution time (15.95s), kappa statistic (87.69 %), F1-score (85.54 %), classification rate (88.82 %), false-negative rate (14.28 %), precision rate (86.67 %), convergence (it convergent towards the iteration 90), stability (it is more stable with mean deviation standard equal to 0.12 %), and complexity (it requires much lower time and space computational complexity).

At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for extracting the features from collected unstructured data and Feedforward Neural Network (FFNN) to compute both positive and negative sentimental scores. Then, we used the Mamdani Fuzzy System (MFS) as a fuzzy classifier to classify the outcomes of the two used deep (CNN+FFNN) learning models in three classes, which are: Neutral, Negative, and Positive. Also, to prevent the long execution time taking by our hybrid proposed FDLC, we have implemented our proposal under the Hadoop cluster. An experimental comparative study between our FDLC and some other suggestions from the literature is performed to demonstrate our offered classifier's effectiveness. The empirical result proved that our FDLC performs better than other classifiers in terms of true positive rate, true negative rate, false positive rate, false negative rate, error rate, precision, classification rate, kappa statistic, F1-score and time consumption, complexity, convergence, and stability. INDEX TERMS Deep learning, convolutional neural network (CNN), sentiment analysis, feedforward neural network (FFNN), fuzzy logic, Hadoop framework, MapReduce, Hadoop Distributed File System (HDFS). I. INTRODUCTION Social network platforms like Instagram, Twitter, Youtube, LinkedIn, and Facebook have been considered essential and indispensable in our daily activities. Day-today , billions of social media users disseminate billions of personal or professional posts [1]. For example, Marketers use social media to spread professional posts that endeavor to present, promote, advertise, and market their products, services, events, and brand names. On the other hand, the customers interact The associate editor coordinating the review of this manuscript and approving it for publication was Jing Bi. with the marketers' posts by express their feelings, opinions, ideas, attitudes about the presented products or services [2]. Further, the marketers gather the customer's feedback, study, and analyze it using the sentiment analysis tool. The main objective from they are doing these operations is to improve the quality of their products and services, enhance their offerings by adding other privileges, and improve their brand performance [1], [2]. Sentiment Analysis (SA) plays a significant role in Business Intelligence (BI). In BI, it uses to get responses for questions such as, 'Why is product sales so low?', 'Have customer's needs are fully satisfied by utilizing our products?',

Big Data (BD) is associated with a new generation of technologies and architectures which can harness the value of extremely large volumes of very varied data through real time processing and analysis. It involves changes in (1) data types, (2) accumulation speed, and (3) data volume. This paper presents the main concepts related to the BD paradigm, and introduces architectures and technologies for BD and BD sets. The integration of BD with the Hadoop Framework is also underlined. BD has attracted a lot of attention in the public sector due to the newly emerging technologies that allow the availability of network access. The volume of different types of data has exponentially increased. Some applications of BD in the public sector in Romania are briefly presented.

Twitter, a popular social media platform, has become a rich source of user-generated content. The classification of Twitter users based on their characteristics and behavior has gained significant attention. Deep learning techniques, with their ability to capture complex patterns and representations, have emerged as powerful tools for Twitter user classification. This research article presents a comprehensive review of deep learning approaches for Twitter user classification. We discuss various deep learning architectures, pretraining techniques, and transfer learning strategies used in the classification task. Through a thorough analysis of existing studies, we highlight the strengths and limitations of deep learning approaches and provide recommendations for future research in this field.

21 st Century population management is an excellent challenge for all of us. The current population of India in 2022 is 1,406,631,776, a 0.95% increase from 2021. It is obvious that people need more houses in this situation in order to meet their needs. Consequently, it creates a burden for the real estate industry. It is nearly impossible for a general person to find the estimated price for the house that he wants, as well as to compare the prices of different houses. It is common for the general public to have no knowledge of the real estate market. For this reason, they are forced to pay a lot for information about the house they are interested in. As a result, there is such a high overall house price because of the fact that a middleman or a group of middlemen take an extra fee for this information from the buyer. To overcome this problem for the general public, we propose a supervised machine learning algorithm that will predict estimated home prices in Bengaluru, the capital and the largest city of the Indian state of Karnataka, based on the buyer's preferences such as location, total_sqft, bath, and bhk. The algorithm will determine the best fit.

Massive volumes of multidimensional array-based spatiotemporal data are generated by climate observations and model simulations. The growth in climate data leads to new opportunities for climate studies at multiple spatial and temporal scales. Managing, analyzing and processing of big climate data is considered to be challenging because of huge data volume. In this work multidimensional climate data such as precipitation and temperature are processed and analyzed in the Spark MapReduce Platform, since Spark platform is computationally more efficient than Hadoop-MapReduce Platform of same configuration. In temporal scale monthly and seasonal analysis of climate data has been carried out to understand the regional climate. The prediction of Rainfall on monthly and seasonal time scales is very much important for planning and devising agricultural strategies and disaster management, etc. As the prediction of climate state is very challenging, in this study an attempt is being made for the prediction of the rainfall using the time series analysis in the same framework. As a test case the time series approach such as Auto Regression Integrated Moving Average (ARIMA) has been used for the prediction of rainfall. The proposed approach is evaluated and found to be accurate in the analysis and prediction of climate data and this will surely guide for the researcher for better understanding of the climate and its application to multiple sectors.

In this paper we investigate the use of a multimodal feature learning approach, using neural network based models such as Skip-gram and Denoising Autoencoders, to address sentiment analysis of micro-blogging content, such as Twitter short messages, that are composed by a short text and, possibly, an image. The approach used in this work is motivated by the recent advances in: i) training language models based on neural networks that have proved to be extremely efficient when dealing with web-scale text corpora, and have shown very good performances when dealing with syntactic and semantic word similarities; ii) unsupervised learning, with neural networks, of robust visual features, that are recoverable from partial observations that may be due to occlusions or noisy and heavily modified images. We propose a novel architecture that incorporates these neural networks, testing it on several standard Twitter datasets, and showing that the approach is efficient and obtains good classification results.

CONTEXT is vital in formulating intelligent classifications and responses, especially under uncertainty. In a standard feed-forward neural network (FFNN), context comes in the form of information encoded in the input vector and trained in weight parameters. However, useful information can also be present in the temporal nature of the input vectors, or from past internal states of a network. Future outputs can achieve better accuracy by observing transient trends in the input data, or by utilizing key memories from distant inputs which could be crucial to formulating a correct output. By providing a neural network with an architecture for storing and maintaining memories this additional context can be effectively leveraged. A simple implementation of memory in a neural network would be to write inputs to external memory and use this to concatenate additional inputs into a neural network. For noisy analog inputs, memory inputs pulled from Gaussian distributions can act to preprocess a...

The increasing demand for information and rapid growth of big data has dramatically increased textual data. The amount of different kinds of data has led to the overloading of information. For obtaining useful text information, the classification of texts is considered an imperative task. This paper develops a technique for text classification in big data using the MapReduce model. The goal is to design a hybrid optimization algorithm for classifying the text. Here, the pre-processing is done with the steaming process and stop word removal. In addition, the Extraction of imperative features is performed wherein SentiWordNet features, contextual features, and thematic features are generated. Furthermore, the selection of optimal features is performed using Tanimoto similarity. The Tanimoto similarity method estimates the similarity between the features and selects the relevant features with higher feature selection accuracy. After that, a deep residual network is utilized for dynamic...

Increasing demands for information and the rapid growth of big data have dramatically increased the amount of textual data. In order to obtain useful text information, the classification of texts is considered an imperative task. Accordingly, this article will describe the development of a hybrid optimization algorithm for classifying text. Here, pre-processing was done using the stemming process and stop word removal. Additionally, we performed the extraction of imperative features and the selection of optimal features using the Tanimoto similarity, which estimates the similarity between features and selects the relevant features with higher feature selection accuracy. Following that, a deep residual network trained by the Adam algorithm was utilized for dynamic text classification. Dynamic learning was performed using the proposed Rider invasive weed optimization (RIWO)-based deep residual network along with fuzzy theory. The proposed RIWO algorithm combines invasive weed optimization (IWO) and the Rider optimization algorithm (ROA). These processes are carried out under the MapReduce framework. Our analysis revealed that the proposed RIWO-based deep residual network outperformed other techniques with the highest true positive rate (TPR) of 85%, true negative rate (TNR) of 94%, and accuracy of 88.7%.

Emotion processing has been a very intense domain of investigation in data analysis and NLP during the previous few years. Currently, the algorithms of the deep neural networks have been applied for opinion mining tasks with good results. Among various neuronal models applied for opinion mining a deep belief network (DBN) model has gained more attention. In this proposal, we have developed a combined classifier based on fuzzy Vader lexicon and a parallel deep belief network for emotion analysis. We have implemented multiple pretreatment techniques to improve the quality and soundness of the data and eliminate disturbing data. Afterward, we have performed a semi-automatic dataset labeling using a combination of two different methods: Mamdani's fuzzy system and Vader lexicon. As well, we have applied four feature extractors, which are: GloVe, TFIDF (Trigram), TFIDF (Bigram), TFIDF (Unigram) with the aim of transforming each incoming tweet into a digital value vector. In addition, we have integrated three feature selectors, namely: The ANOVA method, the chi-square approach and the mutual information technique with the objective of selecting the most relevant features. Further, we have implemented the DBN as classifier for classifying each inputted tweet into three categories: neutral, positive or negative. At the end, we have deployed our proposed approach in parallel way employing both Hadoop and Spark framework with the purpose of overcoming the problem of long runtime of massive data. Furthermore, we have carried out a comparison between our newly suggested hybrid approach and alternative hybrid models available in the literature. From the experimental findings, it was found that our suggested vague parallel approach is more powerful than the baseline patterns in terms of false negative rate (1.33%), recall (99.75%), runtime (32.95s), convergence, stability, F1 score (99.53%), accuracy (98.96%), error rate (1.04%), kappa-Static (99.1%), complexity, false positive rate (0.25%), precision rate (97.59%) and specificity rate (98.67%). As a conclusion, our vague parallel approach outperforms baseline and deep learning models, as well as certain other approaches chosen from the literature. INDEX TERMS Hadoop, Sentiment analysis, Extractors of features, HDFS, Selectors of features, MapReduce, Fuzzy logic, Deep belief neural network.

At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for extracting the features from collected unstructured data and Feedforward Neural Network (FFNN) to compute both positive and negative sentimental scores. Then, we used the Mamdani Fuzzy System (MFS) as a fuzzy classifier to classify the outcomes of the two used deep (CNN+FFNN) learning models in three classes, which are: Neutral, Negative, and Positive. Also, to prevent the long execution time taking by our hybrid proposed FDLC, we have implemented our proposal under the Hadoop cluster. An experimental comparative study between our FDLC and some other suggestions from the literature is performed to demonstrate our offered classifier's effectiveness. The empirical result proved that our FDLC performs better than other classifiers in terms of true positive rate, true negative rate, false positive rate, false negative rate, error rate, precision, classification rate, kappa statistic, F1-score and time consumption, complexity, convergence, and stability. INDEX TERMS Deep learning, convolutional neural network (CNN), sentiment analysis, feedforward neural network (FFNN), fuzzy logic, Hadoop framework, MapReduce, Hadoop Distributed File System (HDFS). I. INTRODUCTION Social network platforms like Instagram, Twitter, Youtube, LinkedIn, and Facebook have been considered essential and indispensable in our daily activities. Day-today , billions of social media users disseminate billions of personal or professional posts [1]. For example, Marketers use social media to spread professional posts that endeavor to present, promote, advertise, and market their products, services, events, and brand names. On the other hand, the customers interact The associate editor coordinating the review of this manuscript and approving it for publication was Jing Bi. with the marketers' posts by express their feelings, opinions, ideas, attitudes about the presented products or services [2]. Further, the marketers gather the customer's feedback, study, and analyze it using the sentiment analysis tool. The main objective from they are doing these operations is to improve the quality of their products and services, enhance their offerings by adding other privileges, and improve their brand performance [1], [2]. Sentiment Analysis (SA) plays a significant role in Business Intelligence (BI). In BI, it uses to get responses for questions such as, 'Why is product sales so low?', 'Have customer's needs are fully satisfied by utilizing our products?',

At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for extracting the features from collected unstructured data and Feedforward Neural Network (FFNN) to compute both positive and negative sentimental scores. Then, we used the Mamdani Fuzzy System (MFS) as a fuzzy classifier to classify the outcomes of the two used deep (CNN+FFNN) learning models in three classes, which are: Neutral, Negative, and Positive. Also, to prevent the long execution time taking by our hybrid proposed FDLC, we have implemented our proposal under the Hadoop cluster. An experimental comparative study between our FDLC and some other suggestions from the literature is performed to demonstrate our offered classifier's effectiveness. The empirical result proved that our FDLC performs better than other classifiers in terms of true positive rate, true negative rate, false positive rate, false negative rate, error rate, precision, classification rate, kappa statistic, F1-score and time consumption, complexity, convergence, and stability. INDEX TERMS Deep learning, convolutional neural network (CNN), sentiment analysis, feedforward neural network (FFNN), fuzzy logic, Hadoop framework, MapReduce, Hadoop Distributed File System (HDFS). I. INTRODUCTION Social network platforms like Instagram, Twitter, Youtube, LinkedIn, and Facebook have been considered essential and indispensable in our daily activities. Day-today , billions of social media users disseminate billions of personal or professional posts [1]. For example, Marketers use social media to spread professional posts that endeavor to present, promote, advertise, and market their products, services, events, and brand names. On the other hand, the customers interact The associate editor coordinating the review of this manuscript and approving it for publication was Jing Bi. with the marketers' posts by express their feelings, opinions, ideas, attitudes about the presented products or services [2]. Further, the marketers gather the customer's feedback, study, and analyze it using the sentiment analysis tool. The main objective from they are doing these operations is to improve the quality of their products and services, enhance their offerings by adding other privileges, and improve their brand performance [1], [2]. Sentiment Analysis (SA) plays a significant role in Business Intelligence (BI). In BI, it uses to get responses for questions such as, 'Why is product sales so low?', 'Have customer's needs are fully satisfied by utilizing our products?',

At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for extracting the features from collected unstructured data and Feedforward Neural Network (FFNN) to compute both positive and negative sentimental scores. Then, we used the Mamdani Fuzzy System (MFS) as a fuzzy classifier to classify the outcomes of the two used deep (CNN+FFNN) learning models in three classes, which are: Neutral, Negative, and Positive. Also, to prevent the long execution time taking by our hybrid proposed FDLC, we have implemented our proposal under the Hadoop cluster. An experimental comparative study between our FDLC and some other suggestions from the literature is performed to demonstrate our offered classifier's effectiveness. The empirical result proved that our FDLC performs better than other classifiers in terms of true positive rate, true negative rate, false positive rate, false negative rate, error rate, precision, classification rate, kappa statistic, F1-score and time consumption, complexity, convergence, and stability. INDEX TERMS Deep learning, convolutional neural network (CNN), sentiment analysis, feedforward neural network (FFNN), fuzzy logic, Hadoop framework, MapReduce, Hadoop Distributed File System (HDFS). I. INTRODUCTION Social network platforms like Instagram, Twitter, Youtube, LinkedIn, and Facebook have been considered essential and indispensable in our daily activities. Day-today , billions of social media users disseminate billions of personal or professional posts [1]. For example, Marketers use social media to spread professional posts that endeavor to present, promote, advertise, and market their products, services, events, and brand names. On the other hand, the customers interact The associate editor coordinating the review of this manuscript and approving it for publication was Jing Bi. with the marketers' posts by express their feelings, opinions, ideas, attitudes about the presented products or services [2]. Further, the marketers gather the customer's feedback, study, and analyze it using the sentiment analysis tool. The main objective from they are doing these operations is to improve the quality of their products and services, enhance their offerings by adding other privileges, and improve their brand performance [1], [2]. Sentiment Analysis (SA) plays a significant role in Business Intelligence (BI). In BI, it uses to get responses for questions such as, 'Why is product sales so low?', 'Have customer's needs are fully satisfied by utilizing our products?',

According to the published reports and studies, the symptoms of the disease caused by the COVID-19 virus have not yet been fully determined. It is a major stress on clinicians to make a correct and consistent decision about whether to apply the test or not, as many factors with extreme uncertainty need to be evaluated at once. In this study, it is aimed to provide assistance to the clinicians by processing the data using fuzzy logic based decision support system at the time of the decision-making process. In the designed fuzzy logic based decision support system, a fuzzy rule-base was created with linguistic information by interpreting the symptoms that are naturally uncertain by experts. With the help of the obtained fuzzy rule base, the input data of symptoms will be processed and the risk of a person being infected will be obtained as an output. As the results of the estimation module constructed with the existing parameters are examined, it is observed to be compatible with the data published before. In this context, a data set with 50 different patients were designed randomly to evaluate the system. For the analysis of the nonlinear mapping obtained with the Mamdani type fuzzy inference system, random test data is used and infection risk at rates varying between 12.5-83% was determined. The fuzzy logic based decision support system for COVID-19 can be accepted as applicable, flexible, and trustworthy for clinicians. It can be said that this system is not only suitable for COVID-19 but also applicable for future epidemics.

Opinion Mining (OM) is a field of Natural Language Processing (NLP) that aims to capture human sentiment in the given text. With the ever-spreading of online purchasing websites, micro-blogging sites, and social media platforms, OM in online social media platforms has picked the interest of thousands of scientific researchers. Because the reviews, tweets and blogs acquired from these social media networks, act as a significant source for enhancing the decision making process. The obtained textual data (reviews, tweets, or blogs) are classified into three different class labels which are negative, neutral and positive for analyzing and extracting relevant information from the given dataset. In this contribution, we introduce an innovative MapReduce improved weighted ID3 decision tree classification approach for OM, which consists mainly of three aspects: Firstly We have used several feature extractors to efficiently detect and capture the relevant data from the given tweets, including N-grams or character-level, Bag-Of-Words, word embedding (GloVe, Word2Vec), FastText, and TF-IDF. Secondly, we have applied a multiple feature selector to reduce the high feature's dimensionality, including Chi-square, Gain Ratio, Information Gain, and Gini Index. Finally, we have employed the obtained features to carry out the classification task using an improved ID3 decision tree classifier, which aims to calculate the weighted information gain instead of information gain used in traditional ID3. In other words, to measure the weighted information gain for the current conditioned feature, we follow two steps: First, we compute the weighted correlation function of the current conditioned feature. Second, we multiply the obtained weighted correlation function by the information gain of this current conditioned feature. This work is implemented in a distributed environment using the Hadoop framework, with its programming framework MapReduce and its distributed file system HDFS. Its primary goal is to enhance the performance of a well-known ID3 classifier in terms of accuracy, execution time, and ability to handle the massive datasets. We have carried out several experiences that aims to assess the effectiveness of our suggested classifier compared to some other contributions chosen from the literature. The experimental results demonstrated that our ID3 classifier works better on COVID-19_Sentiments dataset than other classifiers in terms of Recall (85.72 %), specificity (86.51 %), error rate (11.18 %), false-positive rate (13.49 %), execution time (15.95s), kappa statistic (87.69 %), F1-score (85.54 %), classification rate (88.82 %), false-negative rate (14.28 %), precision rate (86.67 %), convergence (it convergent towards the iteration 90), stability (it is more stable with mean deviation standard equal to 0.12 %), and complexity (it requires much lower time and space computational complexity).

In this modernistic chapter of information and technology, a galactic volume of data generations is happening every moment. Big data is a phrase that is referred to data sets that are not only big or, massive but also having velocity, variety, veracity, and value making it difficult to analyze, visualize, and store using conventional methods. Big data analytics provides an efficient method to analyze and store those varied and complex structured data for unfolding hidden patterns and correlates them. This work focuses on an overview of big data, big data sources, big data analytics, methods, and tools that are applied to big data, storing and managing big data, the diversity of big data in making decisions in different fields, advantages, previous works, future aspects, difficulties, and security issues.

Twitter is one of the social media platforms that has evolved into an incredible environment for users to communicate with friends and other users to trade thoughts, videos, and photographs that reflect their present mood. Using social media allows academics to investigate people' online data, revealing their moods and habits, and analyze their mental states. Since social media is becoming a part of our life, this study implies users' moods by what they post in their tweets. This study will analyze Twitter data for depression sentiment using a verified public internet portal. Social information is presented as an important predictor of depression. This study suggests distinct deep learning classification strategies which can perform as a competent and adaptable strategy and have already been effectively utilized in Natural Language Processing jobs to explore the impact of depression diagnosis on Twitter. LSTM and Convolutional Neural Networks were used to analyze Twitter data for depression analysis. Bi-LSTM proven the highest accuracy of 95% for real time depression analysis. We also developed a depression analysis tool based on this study.

Currently, remote sensing is widely used in environmental monitoring applications, mostly air quality mapping and climate change supervision. However, satellite sensors occur massive volumes of data in near-real-time, stored in multiple formats and are provided with high velocity and variety. Besides, the processing of satellite big data is challenging. Thus, this study aims to approve that satellite data are big data and proposes a new big data architecture for satellite data processing. The developed software is enabling an efficient remote sensing big data ingestion and preprocessing. As a result, the experiment results show that 86 percent of the unnecessary daily files are discarded with a data cleansing of 20 percent of the erroneous and inaccurate plots. The final output is integrated into the Hadoop system, especially the HDFS, HBase, and Hive, for extra calculation and processing.

Annually, over three million people in North America suffer concussions. Every age group is susceptible to concussion, but youth involved in sporting activities are particularly vulnerable, with about 6% of all youth suffering a concussion annually. Youth who suffer concussion have also been shown to have higher rates of suicidal ideation, substance and alcohol use, and violent behaviors. A significant body of research over the last decade has led to changes in policies and laws intended to reduce the incidence and burden of concussions. However, it is also clear that youth engaging in high-risk activities like sport often underreport concussion, while others may embellish reports for specific purposes. For such policies and laws to work, they must operate effectively within a facilitative social context so understanding the culture around concussion becomes essential to reducing concussion and its consequences. We present an automated deep neural network approach to analyze tweets ...

Sentiment analysis is application of natural language processing for understanding the opinions or views of public on various topics. This is also popularly known as opinion mining, the system collects, analyses and examines the sentiments present in the form of tweets. Our proposed model extracts the sentiment of the tweets and classifies them using CNN-BiLSTM which is a technique of deep learning and uses Word2Vec as word embedding layer. The Sentiment140 dataset is generated from Twitter API which consists 1.6 million tweets. BiLSTM cell state based on memory is used for tweets classification Sentiments are published on Social media in the form of texts for expressing social support, happiness, anger, friendship etc. Using deep learning approach, we will be classifying the tweets as positive or negative. CNN-BiLSTM is an effective technique as compared to others like SVM, Naive Bayes Classifier and

This paper presents a novel 1-D sentiment classifier trained on the benchmark IMDB dataset. The classifier is a 1-D convolutional neural network with repeated convolution and max pooling layers. The main contribution of this work is the demonstration of a deconvolution technique for 1-D convolutional neural networks that is agnostic to specific architecture types. This deconvolution technique enables text classification to be explained, a feature that is important for NLP-based decision support systems, as well as being an invaluable diagnostic tool.

Sentiment analysis has been an important topic of discussion from two decades since Lee published his first paper on the sentimental analysis in 2002. Apart from the sentimental analysis in English, it has spread its wing to other natural languages whose significance is very important in a multi linguistic country like India. The traditional approaches in machine learning have paved better accuracy for the Analysis. Deep Learning approaches have gained its momentum in recent years in sentimental analysis. Deep learning mimics the human learning so expectations are to meet higher levels of accuracy. In this paper we have implemented sentimental analysis of tweets in South Indian language Malayalam. The model used is Recurrent Neural Networks Long Short-Term Memory, a deep learning technique to predict the sentiments analysis. Achieved accuracy was found increasing with quality and depth of the datasets. 

The government is seeking preventive steps to reduce the risk of the spread of Covid-19, one of which is social restrictions that have become popular with social distancing and physical distancing. One way to assess whether the steps taken by the government regarding social and physical distancing are accepted or not by the community is by conducting sentiment analysis. The process of sentiment analysis is carried out using a variant of the Recurrent Neural Network (RNN), namely Long Short-Term Memory (LSTM). In this study, the results obtained from the sentiment analysis, where the public response to social distancing and physical distancing has more positive sentiments than negative sentiments. To measure the accuracy level of sentiment analysis using the Recurrent Neural Network (RNN) algorithm and evaluation of the modeling is done using confusion matrix where the results obtained for the training dataset are 89% accuracy, 89% recall, 89% precision, and 89% F1 Score. Meanwhile, ...

This paper presents an algorithm based on fuzzy logic, devised to identify emotions in corpora of literary texts, called Fuzzy Logic Emotions (FLE) classifier. This algorithm evaluates a sentence to define the class(es) of emotions to which it belongs. For this purpose, it considers three types of linguistic variables (verb, noun and adjective) with associated linguistic values used to qualify the emotion they express. A numerical value is computed for each of these terms within a sentence, based on its frequency and the inverse document frequency (TF-IDF). We have tested our FLE classifier with an evaluation protocol, using a literary corpus in Spanish specially structured for working with the automatic detection of emotions in text. We present encouraging performance results favoring our FLE classifier, when compared to other known algorithms established in the literature used for the detection of emotions in text.

Currently, remote sensing is widely used in environmental monitoring applications, mostly air quality mapping and climate change supervision. However, satellite sensors occur massive volumes of data in near-real-time, stored in multiple formats and are provided with high velocity and variety. Besides, the processing of satellite big data is challenging. Thus, this study aims to approve that satellite data are big data and proposes a new big data architecture for satellite data processing. The developed software is enabling an efficient remote sensing big data ingestion and preprocessing. As a result, the experiment results show that 86 percent of the unnecessary daily files are discarded with a data cleansing of 20 percent of the erroneous and inaccurate plots. The final output is integrated into the Hadoop system, especially the HDFS, HBase, and Hive, for extra calculation and processing.

As part of the Social Media Mining for Health Applications (SMM4H) Shared Task 2020,
our team participated in task 2, the automatic classification of tweets that mention
adverse events associated with medication use. Our general approach was to
implement a variety of preprocessing steps, feature extraction methods and machine
learning as well as deep learning techniques such as Support Vector Machine (SVM),
Random Forest and Neural Networks in order to be able to compare various
approaches to identify those most suitable for this task. Based on our results, CNN and
linear SVM were the best-performing models with F1-scores of 0.5300 and 0.4976,
respectively. Moving forward, future steps include further tuning the hyperparameters
and experimenting with different combinations of layers for the CNN model and
exploring methods for handling the imbalanced dataset including oversampling, under
sampling and generating synthetic data.

Text document classification is an important task for diverse natural language processing based applications. Traditional machine learning approaches mainly focused on reducing dimensionality of textual data to perform classification. This although improved the overall classification accuracy, the classifiers still faced sparsity problem due to lack of better data representation techniques. Deep learning based text document classification, on the other hand, benefitted greatly from the invention of word embeddings that have solved the sparsity problem and researchers focus mainly remained on the development of deep architectures. Deeper architectures, however, learn some redundant features that limit the performance of deep learning based solutions. In this paper, we propose a two stage text document classification methodology which combines traditional feature engineering with automatic feature engineering (using deep learning). The proposed methodology comprises a filter based feature selection (FSE) algorithm followed by a deep convolutional neural network. This methodology is evaluated on the two most commonly used public datasets, i.e., 20 Newsgroups data and BBC news data. Evaluation results reveal that the proposed methodology outperforms the state-of-the-art of both the (traditional) machine learning and deep learning based text document classification methodologies with a significant margin of 7.7% on 20 Newsgroups and 6.6% on BBC news datasets.

Sentiment analysis using stemmed Twitter data from various languages is an emerging research topic. In this paper, we address three data augmentation techniques namely Shift, Shuffle, and Hybrid to increase the size of the training data; and then we use three key types of deep learning (DL) models namely recurrent neural network (RNN), convolution neural network (CNN), and hierarchical attention network (HAN) to classify the stemmed Turkish Twitter data for sentiment analysis. The performance of these DL models has been compared with the existing traditional machine learning (TML) models. The performance of TML models has been affected negatively by the stemmed data, but the performance of DL models has been improved greatly with the utilization of the augmentation techniques. Based on the simulation, experimental, and statistical results analysis deeming identical datasets, it has been concluded that the TML models outperform the DL models with respect to both training-time (TTM) a...

This paper solves the problem which arises in the production of crops by analyzing the various factors using data mining techniques. This system gathers information about the crops that are cultivated from the different place around the states. Farmers have the accessibility to take help of this system to predict the best crop available and the ongoing crop details so that they can have maximum profit. This system uses various criteria such as place, population, crop type, soil type, stock, current requirement, season, no of farmers cropping the same crop, crop duration etc. to predict the crop for farmers.

Sentiment detection of Arabic tweets is interesting research topic and it enables scholars to analyze huge resources of shared opinions in social media websites such as Facebook and tweeter. It is one of the more complex natural language processing tasks due to the informal noisy contents and the rich morphology of Arabic language. Many studies have been investigated for Arabic sentiment analysis. However, most of these works ignore analyzing political Arabic tweets, specifically the Sudanese Arabic dialect. In this paper, a deep learning-based approach is proposed for political Arabic Sentiment Analysis (PASA)of tweets. Our approach employed word embedding with convolutional neural network and long short-term memory network techniques to represent the tweets and extract the feature vectors. After that, the feature vectors are fed to the classifier to detect the sentiments. We conducted a number of experiments using a set of performance evaluation metrics on a political twitter dataset to test the proposed PASA approach. Experimental results showed that the proposed approach outperformed the baseline methods in terms of precision, recall and F-score metrics.

At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for e...

Energy and security remain the main two challenges in Wireless Sensor Networks (WSNs). Therefore, protecting these WSN networks from Denial of Service (DoS) and Distributed DoS (DDoS) is one of the WSN networks security tasks. Traditional packet deep scan systems that rely on open field inspection in transport layer security packets and the open field encryption trend are making machine learning-based systems the only viable choice for these types of attacks. This paper contributes to the evaluation of the use machine learning algorithms in WSN nodes traffic and their effect on WSN network life time. We examined the performance metrics of different machine learning classification categories such as K-Nearest Neighbour (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Gboost, Decision Tree (DT), Naïve Bayes, Long Short Term Memory (LSTM), and Multi-Layer Perceptron (MLP) on a WSN-dataset in different sizes. The test results proved that the statistical and logical classification categories performed the best on numeric statistical datasets, and the Gboost algorithm showed the best performance compared to different algorithms on average of all performance metrics. The performance metrics used in these validations were accuracy, F1-score, False Positive Ratio (FPR), False Negative Ratio (FNR), and the training execution time. Moreover, the test results showed the Gboost algorithm got 99.6%, 98.8%, 0.4% 0.13% in accuracy, F1-score, FPR, and FNR, respectively. At training execution time, it obtained 1.41 s for the average of all training time execution datasets. In addition, this paper demonstrated that for the numeric statistical data type, the best results are in the size of the dataset ranging from 3000 to 6000 records and the percentage between categories is not less than 50% for each category with the other categories. Furthermore, this paper investigated the effect of Gboost on the WSN lifetime, which resulted in a 32% reduction compared to other Gboost-free scenarios.

There is a need to extract meaningful information from big data, classify it into different categories, and predict end-user behavior or emotions. Large amounts of data are generated from various sources such as social media and websites. Text classification is a representative research topic in the field of natural-language processing that categorizes unstructured text data into meaningful categorical classes. The long short-term memory (LSTM) model and the convolutional neural network for sentence classification produce accurate results and have been recently used in various natural-language processing (NLP) tasks. Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long-term dependencies between word sequences hence are better used for text classification. However, even with the hybrid approach that leverages the powers of these two deep-learning model...

In broad, three machine learning classification algorithms are used to discover correlations, hidden patterns, and other useful information from different data sets known as big data. Today, Twitter, Facebook, Instagram, and many other social media networks are used to collect the unstructured data. The conversion of unstructured data into structured data or meaningful information is a very tedious task. The different machine learning classification algorithms are used to convert unstructured data into structured data. In this paper, the authors first collect the unstructured research data from a frequently used social media network (i.e., Twitter) by using a Twitter application program interface (API) stream. Secondly, they implement different machine classification algorithms (supervised, unsupervised, and reinforcement) like decision trees (DT), neural networks (NN), support vector machines (SVM), naive Bayes (NB), linear regression (LR), and k-nearest neighbor (K-NN) from the co...

Online reviews and feedback of a product plays a vital role in human tendency to purchase those products. To affect the product sale spammer generates fake reviews on online social media platform. To identify spam reviews and spammer communities is the area of interest of this research work. In literature work, various spam detection techniques are proposed based on Review-Behavioral (RB) Based features, Review-Linguistic (RL) Based Features, User-Behavioral (UB) Based Features are explained but none of the technique provide a simultaneous study of these features and weighting of the features along with finding the relationship among the spam users. The proposed work generates a hybrid feature selection method which merge linguistic based features and behavioral features along with NLP processing and sentiment analysis. Also deep learning classification is used. The results show 91 % accuracy for detecting spam reviews.

In the era of web 2.0, online forums, blogs and Twitter are becoming primary sources for sharing views, opinions and comments about different topics. Classifying these views, opinions and comments is known as sentiment analysis which is an active research area. Sentiment analysis has vast applications in different fields of life, such as marketing, e-commerce and business. Under the umbrella of sentiment analysis, sentiment quantification that deals with estimating relative frequency of class of interest is being investigated by researchers nowadays. In sentiment quantification, exploring effect of new features and comparison of diverse types of classifiers to assess their effectiveness needs further investigation. In this paper, we explore diverse feature sets and classifiers for sentiment quantification. In addition, empirical performance analysis of conventional machine learning techniques, ensemble-based methods and state-ofthe-art deep learning algorithms on basis of features set, is performed. The computed results show that the diverse features sets affect the performance of classifiers in sentiment quantification. The results also confirm that the deep learning techniques perform better than the conventional machine learning algorithms.

This paper explores the combination of two deep learning techniques that consists of convolutional neural networks (CNN) and long short-term memory recurrent neural networks (LSTM-RNN) as a hybrid approach to sentence classification. The technique used CNN in feature extraction, followed by LSTM-RNN to build a classifier. In the text mining field, the performance mostly depends on word features that we need to utilize the distinctive point of CNN to re-organize the feature set for training in the LSTM-RNN layer. In feature initialization, we used a pre-trained GloVe dataset of Common Crawl to reproduce our existing dataset that includes research articles, which were extracted into a set of sentences of success factors relationship statements. We hypothesize that using the hybrid deep learning technique, CNN and LSTM-RNN, with word embedding, GloVe, can improve the performance of sentence classification in terms of recall and precision from the prior work that used only a single CNN ...

Analysis and deciphering code-mixed data is imperative in academia and industry, in a multilingual country like India, in order to solve problems apropos Natural Language Processing. This paper proposes a bidirectional long short-term memory (BiLSTM) with the attention-based approach, in solving the hope speech detection problem. Using this approach an F1-score of 0.73 (9thrank) in the Malayalam-English data set was achieved from a total of 31 teams who participated in the competition.

The increase in covid-19 positive patients in Indonesia, especially in West Java, is unpredictable, resulting in unpreparedness in dealing with covid-19 cases. People in monitoring and patients under supervision are the category that is breast-positive patients after passing the incubation period for 14 days. Fuzzy logic is one derivative of artificial intelligence that is able to predict a thing.The study used the fuzzy logic of the Tsukamoto method to predict the percentage increase in positive cases of covid-19 with measures performed are fuzzification, rule formation, inference, and defuzzification. The results showed a 4.5% error rate indicating that predicting covid-19 using the fuzzy logic of the Tsukamoto method was successful.

The government is seeking preventive steps to reduce the risk of the spread of Covid-19, one of which is social restrictions that have become popular with social distancing and physical distancing. One way to assess whether the steps taken by the government regarding social and physical distancing are accepted or not by the community is by conducting sentiment analysis. The process of sentiment analysis is carried out using a variant of the Recurrent Neural Network (RNN), namely Long Short-Term Memory (LSTM). In this study, the results obtained from the sentiment analysis, where the public response to social distancing and physical distancing has more positive sentiments than negative sentiments. To measure the accuracy level of sentiment analysis using the Recurrent Neural Network (RNN) algorithm and evaluation of the modeling is done using confusion matrix where the results obtained for the training dataset are 89% accuracy, 89% recall, 89% precision, and 89% F1 Score. Meanwhile, ...

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, K -nearest neighbors, random forest, logistic regression, deci...