Journal articles on the topic 'Online Bayes point machine'
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Aljwari, Fatima, Wahaj Alkaberi, Areej Alshutayri, Eman Aldhahri, Nahla Aljojo, and Omar Abouola. "Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News." Postmodern Openings 13, no. 1 Sup1 (March 14, 2022): 01–14. http://dx.doi.org/10.18662/po/13.1sup1/411.
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There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't look at what makes those fake news spread. The threat grows, and at some point, it gets out of hand. That's why this paper is trying to figure out how to predict the features that make Arabic online fake news spread. It's using Naive Bayes, Logistic Regression, and Random forest of Machine Learning to do this. Online news stories that were made up were used. They are found by using Term Frequency-Inverse Document Frequency (TF-IDF). The best partition for testing and validating the prediction was chosen at random and used in the analysis. So, all three machine learning classifications for predicting fake news in Arabic online were done. The results of the experiment show that Random Forest Classifier outperformed the other two algorithms. It had the best TF-IDF with an accuracy of 86 percent. Naive Bayes had an accuracy rate of 84%, and Logistic Regression had an accuracy rate of 85%, so they all did well. As such, the model shows that the features in TF-IDF are the most essential point about the content of an online Arabic fake news.
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Chairani, Chairani, Widyawan Widyawan, and Sri Suning Kusumawardani. "Machine Learning Untuk Estimasi Posisi Objek Berbasis RSS Fingerprint Menggunakan IEEE 802.11g Pada Lantai 3 Gedung JTETI UGM." JURNAL INFOTEL - Informatika Telekomunikasi Elektronika 7, no. 1 (May 10, 2015): 1. http://dx.doi.org/10.20895/infotel.v7i1.23.
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Penelitian ini membahas tentang estimasi posisi (localization) objek dalam gedung menggunakan jaringan wireless atau IEEE 802.11g dengan pendekatan Machine Learning. Metode pada pengukuran RSS menggunakan RSS-based fingerprint. Algoritma Machine Learning yang digunakan dalam memperkirakan lokasi dari pengukuran RSS-based menggunakan Naive Bayes. Localization dilakukan pada lantai 3 gedung Jurusan Teknik Elektro dan Teknologi Informasi (JTETI) dengan luas 1969,68 m2 dan memiliki 5 buah titik penempatan access point (AP). Untuk membentuk peta fingerprint digunakan dimensi 1 m x 1 m sehingga terbentuk grid sebanyak 1893 buah. Dengan menggunakan software Net Surveyor terkumpul data kekuatan sinyal yang diterima (RSS) dari jaringan wireless ke perangkat penerima (laptop) sebanyak 86.980 record. Hasil nilai rata-rata error jarak estimasi untuk localization seluruh ruangan di lantai 3 dengan menggunakan algoritma Naive Bayes pada fase offline tahap learning adalah 6,29 meter. Untuk fase online dan tahap post learning diperoleh rata-rata error jarak estimasi sebesar 7,82 meter.
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Gupta, Kanika, and Vaishnavi Mall. "COMPARATIVE ANALYSIS OF CLASSIFICATION TECHNIQUES FOR CREDIT CARD FRAUD DETECTION." International Research Journal of Computer Science 9, no. 2 (March 4, 2022): 9–15. http://dx.doi.org/10.26562/irjcs.2022.v0902.003.
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Nowadays, in the global computing environment, online payments are a necessary evil as it makes payment conveniently easier and can be done via an ample of available options like a Credit card, Debit Card, Net Banking, PayPal, Paytm available to make payments easier. The most common mode of payment used in online shopping is Credit Card as it is easier for the customers to directly transfer money from one account to another; without the withdrawal of cash at any point. However, this easy payment mode has opened up paths for multiple frauds which involve theft or illegal tampering of data of the credit card owner. Thus, with the increasing number of fraud cases and losses, it is important to find the best solution to detect credit card fraud as well as minimize the number of frauds in online systems. With the analysis of different sets of research performed on the given problem statement, we have concluded that the issue requires a substantial amount of predictions and application of machine learning to find the accuracy score of those commonly used algorithms to predict which of these three state-of-art-algorithms - Naive Bayes, Logistic Regression and K Neighbours, is best suitable to carry out the research in this area. In order to support our findings, we apply two different approaches i.e. with sampling and without sampling on these algorithms against the same dataset. We claim on the basis of our results that K Neighbours outperformed all in both the approaches and is more suitable to carry forward the fraud detection research using machine learning. The analysis will be useful for those working to derive anti-fraud strategies to predict the fraud patterns and reduce the risk during hefty transactions.
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Liu, Guoman, Yufeng Luo, and Jing Sheng. "Research on Application of Naive Bayes Algorithm Based on Attribute Correlation to Unmanned Driving Ethical Dilemma." Mathematical Problems in Engineering 2022 (August 1, 2022): 1–9. http://dx.doi.org/10.1155/2022/4163419.
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At present, unmanned driving technology has made great progress, while those research on its related ethical issues, laws, and traffic regulations are relatively lagging. In particular, it is still a problem how unmanned vehicles make a decision when they encounter ethical dilemmas where traffic collision is inevitable. So it must hinder the application and development of unmanned driving technology. Firstly, 1048575 survey data collected by Moral Machine online experiment platform is analyzed to calculate the prior probability that the straight being protector or sacrificer in ethical dilemmas with single feature. Then, 116 multifeature ethical dilemmas are designed and surveyed. The collected survey data are analyzed to determine decision-making for these ethical dilemmas by adopting the majority principle and to calculate correlation coefficient between attributes, then an improved Naive Bayes algorithm based on attribute correlation (ACNB) is established to solve the problem of unmanned driving decision in multifeature ethical dilemmas. Furthermore, these ethical dilemmas are used to test and verify traditional NB, ADOE, WADOE, CFWNB, and ACNB, respectively. According to the posterior probability that the straight being protector or sacrificer in those ethical dilemmas, classification and decision are made in these ethical dilemmas. Then, the decisions based on these algorithms are compared with human decisions to judge whether these decisions are right. The test results show that ACNB and CFWNB are more consistent with human decisions than other algorithms, and ACNB is more conductive to improve unmanned vehicle’s decision robustness than NB. Therefore, applying ACNB to unmanned vehicles has a good role, which will provide a new research point for unmanned driving ethical decision and a few references for formulating and updating traffic laws and regulations related to unmanned driving technology for traffic regulation authorities.
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Wieczorkowski, Jędrzej, and Aleksandra Suwińska. "Mowa nienawiści w mediach społecznościowych – możliwości automatycznej detekcji i eliminacji." Zarządzanie Mediami 9, no. 4 (December 31, 2021): 681–93. http://dx.doi.org/10.4467/23540214zm.21.037.14580.
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Hate Speech on Social Media – The Possibility of Automatic Detection and Elimination The article deals with the issues of hate speech and other forms of verbal aggression on the Internet as well as the possibility of their automatic detection. The paper discusses the studies confirming the partial effectiveness of text mining methods in the automatic detection of hate speech on social media. Hate speech is related to verbal aggression resulting from belonging to a group (national, racial, religious, etc.) and has become a significant problem in the social and economic context. Automatic detection significantly support the management of online news websites and social media due to the moderation of the received content. Moreover, eliminating online hate speech reduces its negative social and economic effects. The linguistic and cultural specificity of the hate speech are the problem, and the gap so far is solving the problem in Polish conditions. The study used the Tweeter database. Then, methods such as artificial neural networks, naïve Bayes classifier and support vector machine were used. The obtained results confirm the thesis about the possibility of using text mining methods in the process of reducing hate speech, but at the moment the described methods do not allow for full automation of the elimination of such content. The issue was presented in the article primarily in the context of the significance and scale of the problem and the possibility of solving it, and less from the point of view of the technical details.
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Poernomo, Abimanyu Dharma, and Suharjito Suharjito. "Indonesian online travel agent sentiment analysis using machine learning methods." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (April 1, 2019): 113. http://dx.doi.org/10.11591/ijeecs.v14.i1.pp113-117.
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Many companies use social media to support their business activities. Three leading online travel agent such as Traveloka, Tiket.com, and Agoda use Facebook for supporting their business as customer service tool. This study is to measure customer satisfaction of Traveloka, Tiket.com, and Agoda by analyzing Facebook posts and comments data from their fan pages. That data will be analyzed with three machine learning algorithms such as K-Nearest Neighbors (KNN), Naïve Bayes, and Support Vector Machine (SVM) to determine the sentiment. From the classification results, data will be selected with the highest f-score to be used to calculate the Net Sentiment Score used to measure customer satisfaction. The result shows that KNN result better than Naive Bayes and SVM based on f-score. Based on Net Sentiment Score shows companies that get the highest satisfaction value of Traveloka followed by Tiket.com and Agoda
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Kabeer, Ms Shama. "Cyberbullying Detection System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 2059–63. http://dx.doi.org/10.22214/ijraset.2021.38264.
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Abstract: Cyberbullying is an online form of harassment. By posting, commenting, sending, or distributing personal, derogatory, false, or nasty stuff about others that can shame or humiliate them, this conduct is done with the goal of harming others. Once such content is published on the internet, it remains accessible indefinitely. This activity is considered unlawful, and it is more widespread among children and teenagers. Cyberbullying is an online epidemic that has the potential to result in devastating outcomes such as violence and suicide, and so must be dealt with swiftly and properly. To detect bullying behavior in textual messages, a real-time cyberbullying detection system based on machine learning—Naïve Bayes Algorithm is presented. The model was created to determine whether a tweet was bullying or non-bullying in nature. Also, to assist victims in dealing with bullying difficulties without their identities being revealed. Keywords: Machine Learning, Cyberbullying, Naïve Bayes, Cybercrimes, Cyberbullying Detection
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Singh, Ankita. "Flexible Machine Learning based Cyberattack Detection using Spatiotemporal Patterns for Distribution Systems." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 15, 2021): 1129–34. http://dx.doi.org/10.22214/ijraset.2021.35232.
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The Article presents a versatile machine learning detection technique which is employed in distribution systems for cyberattacks considering spatiotemporal patterns. Spatiotemporal patterns are identified by the graph Laplacian which are supported on system-wide measurements. A versatile Bayes classifier is employed to coach spatiotemporal patterns which may well be compromised when cyberattacks happen. Cyberattacks are spotted by utilizing flexible Bayes classifier online.
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Pandey, Shalini, Sankeerthi Prabhakaran, N. V. Subba Reddy, and Dinesh Acharya. "Fake News Detection from Online media using Machine learning Classifiers." Journal of Physics: Conference Series 2161, no. 1 (January 1, 2022): 012027. http://dx.doi.org/10.1088/1742-6596/2161/1/012027.
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Abstract With the advancement in technology, the consumption of news has shifted from Print media to social media. The convenience and accessibility are major factors that have contributed to this shift in consumption of the news. However, this change has bought upon a new challenge in the form of “Fake news” being spread with not much supervision available on the net. In this paper, this challenge has been addressed through a Machine learning concept. The algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Naïve Bayes and Logistic regression Classifiers to identify the fake news from real ones in a given dataset and also have increased the efficiency of these algorithms by pre-processing the data to handle the imbalanced data more appropriately. Additionally, comparison of the working of these classifiers is presented along with the results. The model proposed has achieved an accuracy of 89.98% for KNN, 90.46% for Logistic Regression, 86.89% for Naïve Bayes, 73.33% for Decision Tree and 89.33% for SVM in our experiment.
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Rao Jetti, Chandrasekhar, Rehamatulla Shaik, and Sadhik Shaik. "Disease Prediction using Naïve Bayes - Machine Learning Algorithm." International Journal of Science and Healthcare Research 6, no. 4 (October 8, 2021): 17–22. http://dx.doi.org/10.52403/ijshr.20211004.
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It can occur on many occasions that you or a loved one requires urgent medical assistance, but they are unavailable due to unforeseen circumstances, or that we are unable to locate the appropriate doctor for the care. As a result, we will try to incorporate an online intelligent Smart Healthcare System in this project to solve this issue. It's a web-based programmed that allows patients to get immediate advice about their health problems. The aim of the smart healthcare system is to create a web application that can take a user's symptoms and predict diseases, as well as serve as an online consultant for various diseases. We created an expert system called Smart Health Care System, which is used to make doctors' jobs easier. A machine examines a patient at a basic level and recommends diseases that may be present. It begins by inquiring about the patient's symptoms; if the device is able to determine the relevant condition, it then recommends a doctor in the patient's immediate vicinity. The system will show the result based on the available accumulated data. We're going to use some clever data mining techniques here. We use several intelligent data mining techniques to guess the most accurate illness that could be associated with a patient's symptoms, and we use an algorithm (Naive Bayes) to map the symptoms with potential diseases based on a database of many patients' medical records. This system not only makes doctors' jobs easier, but it also benefits patients by getting them the care they need as soon as possible. Keywords: Disease Prediction, Naïve Bayes, Machine Learning Algorithm, Smart Healthcare System.
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Bhargava, Shivangi, and Dr Shivnath Ghosh. "Analysis of Feature Reduction Techniques for Online News Popularity Prediction." SMART MOVES JOURNAL IJOSCIENCE 4, no. 10 (October 13, 2018): 6. http://dx.doi.org/10.24113/ijo-science.v4i10.165.
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News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.
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Quadri, Mir Habeebullah Shah, and R. K. Selvakumar. "Performance of Naïve Bayes in Sentiment Analysis of User Reviews Online." International Journal of Innovative Technology and Exploring Engineering 10, no. 2 (December 10, 2020): 64–68. http://dx.doi.org/10.35940/ijitee.a8198.1210220.
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Both sellers and buyers heavily depend on the opinions of customers in purchasing and selling products online. When it comes to text-based data, sentiment analysis of user reviews has become a prominent facet of machine learning. Text data is generally unstructured which makes opinion mining very challenging. A wide array of pre-processing and post-processing techniques need to be applied. But the major challenge is selecting the right classifier for the job. Naïve Bayes algorithm is a commonly used machine learning classifier when it comes to opinion mining and sentiment analysis. The focus of this survey is to observe and analyze the performance of Naïve Bayes algorithm in sentiment analysis of user reviews online. Recent research from a wide array of use-cases such as sentiment analysis of movie reviews, product reviews, book reviews, blog posts, microblogs and other sources of data have been taken into account. The results show that Naïve Bayes algorithm performs exceptionally well with accuracies between 75% to 99% across the board.
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Suryawanshi, Nikhil Satish, Harsh Shrivastav, Prateek Bajpai, Asmita Orse, and Prof Megha Patil. "Institute Recommendation System Based on Online Review Comments and Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 965–68. http://dx.doi.org/10.22214/ijraset.2022.40788.
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Abstract: Recommendation system has become a necessity in real world. In recommendation system, we are searching any product based on the reviews, if any product shows the best ratings, then we can select that product. Recommendation system has been used in wide range of application like, amazon, Netflix, Flipkart, Facebook etc. Now institution also become a part of recommendation system. In institution recommendation system, we select those colleges which is having a best reviews, which meets our eligibility criteria and our branch also. Recommendation system helps the user to discover the information and settle on a right choices where they do not have the required learning to judge a specific item. Only Naves Bayes algorithm is best algorithm in this system that we can easily check, and recommends them. This system has been used in education. Keywords: Artificial Intelligence, Machine Learning, Artificial Immune System, Naives Bayes, Decision Tree, Collaborative Filtering, Content Filtering, Hybrid Filtering
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Samrat, Ray. "Fraud Detection in E-Commerce Using Machine Learning." BOHR International Journal of Advances in Management Research 1, no. 1 (2022): 7–14. http://dx.doi.org/10.54646/bijamr.002.
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A rise in transactions is being caused by an increase in online customers.We observe that the prevalence of misrepresentation in online transactions is also increasing. Device learning will become more widely used to avoid misrepresentation in online commerce. The goal of this investigation is to identify the best device learning calculation using decision trees, naive Bayes, random forests, and neural networks. The realities to be utilized have not yet been modified. Engineered minority over-testing stability information is made utilizing the strategy framework. The precision of the brain not entirely settled by
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Setiawan, Hendrik, Ema Utami, and Sudarmawan Sudarmawan. "Analisis Sentimen Twitter Kuliah Online Pasca Covid-19 Menggunakan Algoritma Support Vector Machine dan Naive Bayes." Jurnal Komtika (Komputasi dan Informatika) 5, no. 1 (July 15, 2021): 43–51. http://dx.doi.org/10.31603/komtika.v5i1.5189.
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The World Health Organization (WHO) COVID-19 is an infectious disease caused by the Coronavirus which originally came from an outbreak in the city of Wuhan, China in December 2019 which later became a pandemic that occurred in many countries around the world. This disease has caused the government to give a regional lockdown status to give students the status of "at home" for students to enforce online or online lectures, this has caused various sentiments given by students in responding to online lectures via social media twitter. For sentiment analysis, the researcher applies the nave Bayes algorithm and support vector machine (SVM) with the performance results obtained on the Bayes algorithm with an accuracy of 81.20%, time 9.00 seconds, recall 79.60% and precision 79.40% while for the SVM algorithm get an accuracy value of 85%, time 31.60 seconds, recall 84% and precision 83.60%, the performance results are obtained in the 1st iteration for nave Bayes and the 423th iteration for the SVM algorithm
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Lutfi, Anang Anggono, Adhistya Erna Permanasari, and Silmi Fauziati. "Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine." Journal of Information Systems Engineering and Business Intelligence 4, no. 1 (April 28, 2018): 57. http://dx.doi.org/10.20473/jisebi.4.1.57-64.
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The online store is changing people’s shopping behavior. Despite the fact, the potential customer’s distrust in the quality of products and service is one of the online store’s weaknesses. A review is provided by the online stores to overcome this weakness. Customers often write a review using languages that are not well structured. Sentiment analysis is used to extract the polarity of the unstructured texts. This research attempted to do a sentiment analysis in the sales review. Sentiment analysis in sales reviews can be used as a tool to evaluate the sales. This research intends to conduct a sentiment analysis in the sales review of Indonesian marketplace by utilizing Support Vector Machine and Naive Bayes. The reviews of the data are gathered from one of Indonesian marketplace, Bukalapak. The data are classified into positive or negative class. TF-IDF is used to feature extraction. The experiment shows that Support Vector Machine with linear kernel provides higher accuracy than Naive Bayes. Support Vector Machine shows the highest accuracy average. The generated accuracy is 93.65%. This approach of sentiment analysis in sales review can be used as the base of intelligent sales evaluation for online stores in the future.
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Qiang, Yang, Lei Zhang, Zhi Li Sun, Yi Liu, and Xue Bin Bai. "Reliability Analysis Based on Improved Bayes Method of AMSAA Model." Advanced Materials Research 482-484 (February 2012): 2336–40. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.2336.
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Aiming at the defects that failure samples of five-axis NC machine tools is small, traditional reliability analysis is not accurate, this paper presents reliability analysis mode based on improved Bayesian method for AMSAA model. Firstly, we obtain the failure model of NC machine tools meets the AMSAA model according to goodness-of-fit test, and in order to meet the requirements of simplifying engineering calculations, this paper adpots a method of Coefficient equivalent which converts failure Data into index-life data; then using Bayesian methods to estimate reliability parameters for the Index-life data; for the last we proceed point estimation and interval estimation for the MTBF of the machine. Take High-speed five-axis NC machine tools t of VMC650m for example, the result proved that the method can take advantage of a small sample of the equipment to proceed point estimation and interval estimation for MTBF failure data, and provide a reference for the optimization of maintenance strategies and Diagnostic work of the NC machine tools.
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Falasari, Anisa, and Much Aziz Muslim. "Optimize Naïve Bayes Classifier Using Chi Square and Term Frequency Inverse Document Frequency For Amazon Review Sentiment Analysis." Journal of Soft Computing Exploration 3, no. 1 (March 30, 2022): 31–36. http://dx.doi.org/10.52465/joscex.v3i1.68.
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The rapid development of the internet has made information flow rapidly wich has an impact on the world of commerce. Some people who have bought a product will write their opinion on social media or other online site. Long-text buyer reviews need a machine to recognize opinions. Sentiment analysis applies the text mining method. One of the methods applied in sentiment analysis is classification. One of the classification algorithms is the naïve bayes classifier. Naïve bayes classifier is a classification method with good efficiency and performance. However, it is very sensitive with too many features, wich makes the accuracy low. To improve the accuracy of the naïve bayes classifier algorithm it can be done by selecting features. One of the feature selection is chi square. The selection of features with chi square calculation based on the top-K value that has been determined, namely 450. In addition, weighting features can also improve the accuracy of the naïve bayes classifier algorithm. One of the feature weighting techniques is term frequency inverse document frequency (TF-IDF). In this study, using sentiment labelled dataset (field amazon_labelled) obtained from UCI Machine Learning. This dataset has 500 positive reviews and 500 negative reviews. The accuracy of the naïve bayes classifier in the amazon review sentiment analysis was 82%. Meanwhile, the accuracy of the naïve bayes classifier by applying chi square and TF-IDF is 83%.
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Machová, Kristína, Marián Mach, and Kamil Adamišín. "Machine Learning and Lexicon Approach to Texts Processing in the Detection of Degrees of Toxicity in Online Discussions." Sensors 22, no. 17 (August 27, 2022): 6468. http://dx.doi.org/10.3390/s22176468.
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This article focuses on the problem of detecting toxicity in online discussions. Toxicity is currently a serious problem when people are largely influenced by opinions on social networks. We offer a solution based on classification models using machine learning methods to classify short texts on social networks into multiple degrees of toxicity. The classification models used both classic methods of machine learning, such as naïve Bayes and SVM (support vector machine) as well ensemble methods, such as bagging and RF (random forest). The models were created using text data, which we extracted from social networks in the Slovak language. The labelling of our dataset of short texts into multiple classes—the degrees of toxicity—was provided automatically by our method based on the lexicon approach to texts processing. This lexicon method required creating a dictionary of toxic words in the Slovak language, which is another contribution of the work. Finally, an application was created based on the learned machine learning models, which can be used to detect the degree of toxicity of new social network comments as well as for experimentation with various machine learning methods. We achieved the best results using an SVM—average value of accuracy = 0.89 and F1 = 0.79. This model also outperformed the ensemble learning by the RF and Bagging methods; however, the ensemble learning methods achieved better results than the naïve Bayes method.
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Hayati, Hind, Abdessamad Chanaa, Mohammed Khalidi Idrissi, and Samir Bennani. "Doc2Vec &Naïve Bayes: Learners’ Cognitive Presence Assessment through Asynchronous Online Discussion TQ Transcripts." International Journal of Emerging Technologies in Learning (iJET) 14, no. 08 (April 30, 2019): 70. http://dx.doi.org/10.3991/ijet.v14i08.9964.
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Due to the lack of face to face interaction in online learning environment, this article aims essentially to give tutors the opportunity to understand and analyze learners’ cognitive behavior. In this perspective, we propose an automatic system to assess learners’ cognitive presence regarding their social interactions within synchronous online discussions. Combining Natural Language Preprocessing, Doc2Vec document embedding method and machine learning techniques; we first make some transformations and preprocessing to the given transcripts, then we apply Doc2Vec method to represent each message as a vector that will be concatenated with LIWC and context features. The vectors are input data of Naïve Bayes algorithm; a machine learning method; that aims to classify transcripts according to cognitive presence categories.
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Yulia, Eka Rini, and Kusmayanti Solecha. "Implementasi Particle Swarm Optimization (PSO) pada Analysis Sentiment Review Aplikasi Trafi menggunakan Algoritma Naive Bayes (NB)." Jurnal Teknik Komputer 7, no. 1 (February 3, 2021): 25–29. http://dx.doi.org/10.31294/jtk.v7i1.9078.
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Abstract - The development of transportation applications is now getting bigger so that many vendors compete for business in creating transportation mode applications, starting from the quality and quantity so that it is often questioned. With this, the researcher held a transportation application called Trafi to get opinions or comments on applications from people who had used the application and poured it into online media. Of the many comments reviewed to obtain a set of positive and negative forms of data from the text that the researcher will process. For classification data using Naïve Bayes (NB), NB is one of the most popular algorithms for pattern recognition. Apart from simplicity, the Naive Bayes classifier is a popular machine learning technique for text classification, Particle Swarm Optimization (PSO) which combines with the Naive Bayes classification to improve performance. Before use, optimization with PSO in the data set accuracy obtained was 69.50% and after the combination of Naive Bayes and PSO accuracy was 72.34%. Use PSO and Naïve Bayes according to the concept of text mining which aims to find patterns that exist in text, the activity carried out by text mining here is text classification.Abstract - The development of transportation applications is now getting bigger so that many vendors compete for business in creating transportation mode applications, starting from the quality and quantity so that it is often questioned. With this, the researcher held a transportation application called Trafi to get opinions or comments on applications from people who had used the application and poured it into online media. Of the many comments reviewed to obtain a set of positive and negative forms of data from the text that the researcher will process. For classification data using Naïve Bayes (NB), NB is one of the most popular algorithms for pattern recognition. Apart from simplicity, the Naive Bayes classifier is a popular machine learning technique for text classification, Particle Swarm Optimization (PSO) which combines with the Naive Bayes classification to improve performance. Before use, optimization with PSO in the data set accuracy obtained was 69.50% and after the combination of Naive Bayes and PSO accuracy was 72.34%. Use PSO and Naïve Bayes according to the concept of text mining which aims to find patterns that exist in text, the activity carried out by text mining here is text classification.Keywords: Sentiment Analysis, Android Appstore Product Review, Naive Bayes Algorithm
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Billy Anthony Christian Martani and Erwin Budi Setiawan. "Naïve Bayes-Support Vector Machine Combined BERT to Classified Big Five Personality on Twitter." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 6 (December 30, 2022): 1072–78. http://dx.doi.org/10.29207/resti.v6i6.4378.
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Twitter is one of the most popular social media used to interact online. Through Twitter, a person's personality can be determined based on that person's thoughts, feelings, and behavior patterns. A person has five main personalities likes Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. This study will make five personality predictions using the Naïve Bayes method – Support Vector Machine, Synthetic Minority Over Sampling Technique (SMOTE), Linguistic Inquiry Word Count (LIWC), and Bidirectional Encoder from Transformers Representations (BERT). A questionnaire was distributed to people who used Twitter to collect and become a dataset in this research. The dataset obtained will be processed into SMOTE to balance the data. Linguistic Inquiry Word Count is used as a linguistic feature and BERT will be used as a semantic approach. The Naïve Bayes method is used to perform the weighting and the Support Vector Machine is used to classify Big Five Personalities. To help improve accuracy, the Optuna Hyperparameter Tuning method will be added to the Naïve Bayes Support Vector Machine model. This study has an accuracy of 87.82% from the results of combining SMOTE, BERT, LIWC, and Tuning where the accuracy increases from the baseline.
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Abu Samah, Khyrina Airin Fariza, Nur Farhanah Amirah Misdan, Mohd Nor Hajar Hasrol Jono, and Lala Septem Riza. "The Best Malaysian Airline Companies Visualization through Bilingual Twitter Sentiment Analysis: A Machine Learning Classification." JOIV : International Journal on Informatics Visualization 6, no. 1 (March 31, 2022): 130. http://dx.doi.org/10.30630/joiv.6.1.879.
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Online reviews are crucial for business growth and customer satisfaction. There is no exception for the airlines’ company, which places third as the biggest contributor to Malaysia’s Gross Domestic Product. Customer opinions play an important role in maintaining the reputation and improving the quality of service of the airlines. However, there is no specific platform for online review. Most online ratings obtain English, leading to inaccurate results as not all reviews regarding different languages are considered. Airlines currently have no specific platform for online reviews despite being critical for business growth, performance, and customer experience improvement. Hence, this paper proposed implementing a web-based dashboard to visualize the best Malaysian airline companies. The airline companies involved are AirAsia, Malaysia Airlines, and Malindo Air. We designed and developed the proposed study through the bilingual analysis of Twitter sentiment using the Naïve Bayes algorithm. Naïve Bayes algorithm is a machine learning approach to do classification. The tweets extracted were analyzed as metrics that advance airline companies’ online presence. Testing phases have shown that the classifier successfully classified tweets’ sentiment with 93% accuracy for English and 91% for Bahasa. Every feature in the web-based dashboard functions correctly and visualizes a detailed analysis of sentiment. We applied the System Usability Scale to test the study’s usability and managed to get a score of 94.7%. The acceptability score ‘acceptable’ result concluded that the study reflects a good solution and can assist anyone in understanding the public views on airline companies in Malaysia.
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Petiwi, Melati Indah, Agung Triayudi, and Ira Diana Sholihati. "Analisis Sentimen Gofood Berdasarkan Twitter Menggunakan Metode Naïve Bayes dan Support Vector Machine." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 1 (January 25, 2022): 542. http://dx.doi.org/10.30865/mib.v6i1.3530.
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The Covid-19 pandemic in Indonesia has an impact on every sector of life, including the economy. The government implements social activities that make people have to carry out activities at home. Because of this, humans choose to do everything digitally, including ordering food. With the application of public interest in ordering food online, the income of one of the food orders, namely Gojek (Gofood) has increased. However, Gofood has many pros and cons in the community. In this case, many people give their opinion about the use of social media, especially twitter. The purpose of this study was to analyze public opinion on the performance of Gojek (Gofood) in Indonesia. The grouping into three classes, namely positive, negative and neutral classes were tested using the Naïve Bayes and SVM methods and compared the two methods. The analysis of public sentiment regarding Gofood on Twitter resulted in 92.8% worthy neutral, 5.2% worthy positive and 2.0% worthy negative. Comparing the accuracy results, the Support Vector Machine method has greater accuracy than the Naïve Bayes method, with the Support Vector Machine accuracy values of 83% and 98.5%, while the Nave Bayes accuracy values are 74.6% and 91.5% respectively.
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Dwianto, Enos, and Mujiono Sadikin. "Analisis Sentimen Transportasi Online pada Twitter Menggunakan Metode Klasifikasi Naïve Bayes dan Support Vector Machine." Format : Jurnal Ilmiah Teknik Informatika 10, no. 1 (February 8, 2021): 94. http://dx.doi.org/10.22441/format.2021.v10.i1.009.
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Transportasi online merupakan salah satu pilihan bagi masyarakat untuk berkegiatan sehari-hari baik saat bekerja, bepergian dan melakukan aktivitas lain. Salah satu cara untuk mengetahui persepsi masyarakat terhadap layanan transportasi online adalah dengan analisis sentimen seperti yang dilakukan pada penelitian ini. Data yang digunakan merupakan data valid dari sosial media Twitter untuk Transportasi online GrabId dan GojekIndonesia. Teknik analisis sentimen yang digunakan adalah Naïve Bayes Classifier dan metode Support Vector Machine (SVM). Keduanya digunakan untuk membandingkan tanggapan masyarakat dari analisis sentimen data tweet yang telah diklasifikasikan menjadi positif dan negatif. Berdasarkan penelitian ini didapatkan bahwa GrabId menggunakan metode SVM memberikan hasil class precision positif dan negatif yaitu 86.47% dan 46.67%, class recall positif dan negatif yaitu 96.21% dan 18.06%, accuracy 84.08%. Sedangkan untuk GojekIndonesia, metode SVM memberikan hasil yaitu class precision positif dan negatif yaitu 73.90% dan 35.65%, class recall positif dan negatif yaitu 89.84% dan 15.07%, accuracy 69.50%. Dari akurasi yang dihasilkan, metode SVM menghasilkan kinerja terbaik.
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Pal, Riya, Shahrukh Shaikh, Swaraj Satpute, and Sumedha Bhagwat. "Resume Classification using various Machine Learning Algorithms." ITM Web of Conferences 44 (2022): 03011. http://dx.doi.org/10.1051/itmconf/20224403011.
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With the onset of the epidemic, everything has gone online, and individuals have been compelled to work from home. There is a need to automate the hiring process in order to enhance efficiency and decrease manual labour that may be done electronically. If resume categorization were done online, it would significantly save paperwork and human error. The recruiting process has several steps, but the first is resume categorization and verification. Automating the first stage would greatly assist the interview process in terms of speedy applicant selection. Classification of resumes will be performed using Machine Learning Algorithms such as Nave Bayes, Random Forest, and SVM, which will aid in the extraction of skills and show diverse capabilities under appropriate job profile classes. While the abilities are being extracted, an appropriate job profile may be retrieved from the categorised and pre-processed data and shown on the interviewer’s screen. During video interviews, this will aid the interviewer in the selection of candidates.
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Ali, Manal Mostafa. "Arabic sentiment analysis about online learning to mitigate covid-19." Journal of Intelligent Systems 30, no. 1 (January 1, 2021): 524–40. http://dx.doi.org/10.1515/jisys-2020-0115.
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Abstract The Covid-19 pandemic is forcing organizations to innovate and change their strategies for a new reality. This study collects online learning related tweets in Arabic language to perform a comprehensive emotion mining and sentiment analysis (SA) during the pandemic. The present study exploits Natural Language Processing (NLP) and Machine Learning (ML) algorithms to extract subjective information, determine polarity and detect the feeling. We begin with pulling out the tweets using Twitter APIs and then preparing for intensive preprocessing. Second, the National Research Council Canada (NRC) Word-Emotion Lexicon was examined to calculate the presence of the eight emotions at their emotional weight. Third, Information Gain (IG) is used as a filtering technique. Fourth, the latent reasons behind the negative sentiments were recognized and analyzed. Finally, different classification algorithms including Naïve Bayes (NB), Multinomial Naïve Bayes (MNB), K Nearest Neighbor (KNN), Logistic Regression (LR), and Support Vector Machine (SVM) were examined. The experiments reveal that the proposed model performs well in analyzing the perception of people about coronavirus with a maximum accuracy of about 89.6% using SVM classifier. From a practical perspective, the method could be generalized to other topical domains, such as public health monitoring and crisis management. It would help public health officials identify the progression and peaks of concerns for a disease in space and time, which enables the implementation of appropriate preventive actions to mitigate these diseases.
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Buzea, Marius Cristian, Stefan Trausan-Matu, and Traian Rebedea. "Automatic Fake News Detection for Romanian Online News." Information 13, no. 3 (March 14, 2022): 151. http://dx.doi.org/10.3390/info13030151.
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This paper proposes a supervised machine learning system to detect fake news in online sources published in Romanian. Additionally, this work presents a comparison of the obtained results by using recurrent neural networks based on long short-term memory and gated recurrent unit cells, a convolutional neural network, and a Bidirectional Encoder Representations from Transformers (BERT) model, namely RoBERT, a pre-trained Romanian BERT model. The deep learning architectures are compared with the results achieved by two classical classification algorithms: Naïve Bayes and Support Vector Machine. The proposed approach is based on a Romanian news corpus containing 25,841 true news items and 13,064 fake news items. The best result is over 98.20%, achieved by the convolutional neural network, which outperforms the standard classification methods and the BERT models. Moreover, based on irony detection and sentiment analysis systems, additional details are revealed about the irony phenomenon and sentiment analysis field which are used to tackle fake news challenges.
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V.Srinivas, A., Nandyala Bindu, Belide Partha, Katta Meghana, and Vaddula Anirudh. "LOCATION PREDICTION ON TWITTER USING MACHINE LEARNING TECHNIQUES." YMER Digital 21, no. 05 (May 10, 2022): 425–31. http://dx.doi.org/10.37896/ymer21.05/46.
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These days, location prediction of users using online social media generates a lot of research. For decades, researchers have looked on automatic location recognition in relation to or referenced in documents. As one of the most popular online social networking sites, Twitter has attracted a large number of users who send millions of tweets on a regular basis. Because of the global reach of its users and the constant flow of messages, location prediction on Twitter has gotten a lot of attention lately. Tweets, brief, noisy, and rich-natured communications, provide numerous study hurdles to researchers. A general picture of location prediction using tweets is investigated in the suggested framework. Tweet location, in particular, is anticipated based on tweet content. It is very important to outline tweet content and situations highlighted how the difficulties are dependent on certain text inputs. Here, we apply machine learning techniques like as naive bayes, Support Vector Machines, and Decision Trees to estimate the user's location from tweet content.
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Sultana, MD Arsha, Rakesh P, Sandeep M, and Jagadeesh G. "AMAZON PRODUCT REVIEW SENTIMENT ANALYSIS USING MACHINE LEARNING." International Research Journal of Computer Science 8, no. 7 (July 30, 2021): 136–41. http://dx.doi.org/10.26562/irjcs.2021.v0807.001.
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As online marketplaces have been popular during the past decades, online sellers and merchants ask their purchasers to share their opinions about the products they have bought. As a result, millions of reviews are being generated daily which makes it difficult for a potential consumer to make a good decision on whether to buy the product. Analyzing this enormous amount of opinions is also hard and time-consuming for product manufacturers. But in this prospering day of machine learning, going through thousands of reviews would be much easier if a model is used to polarize those reviews and learn from it. This thesis considers the problem of classifying reviews by their overall semantic (positive, negative, or neutral). To conduct the study different supervised machine learning techniques, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, and Logistic Regression have been attempted on products review dataset from Amazon. Their accuracies have then been compared.
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Daud, Shahzada, Muti Ullah, Amjad Rehman, Tanzila Saba, Robertas Damaševičius, and Abdul Sattar. "Topic Classification of Online News Articles Using Optimized Machine Learning Models." Computers 12, no. 1 (January 9, 2023): 16. http://dx.doi.org/10.3390/computers12010016.
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Much news is available online, and not all is categorized. A few researchers have carried out work on news classification in the past, and most of the work focused on fake news identification. Most of the work performed on news categorization is carried out on a benchmark dataset. The problem with the benchmark dataset is that model trained with it is not applicable in the real world as the data are pre-organized. This study used machine learning (ML) techniques to categorize online news articles as these techniques are cheaper in terms of computational needs and are less complex. This study proposed the hyperparameter-optimized support vector machines (SVM) to categorize news articles according to their respective category. Additionally, five other ML techniques, Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Naïve Bayes (NB), were optimized for comparison for the news categorization task. The results showed that the optimized SVM model performed better than other models, while without optimization, its performance was worse than other ML models.
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Hassan, Mahmudul, Shahriar Shakil, Nazmun Nessa Moon, Mohammad Monirul Islam, Refath Ara Hossain, Asma Mariam, and Fernaz Narin Nur. "Sentiment analysis on Bangla conversation using machine learning approach." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (October 1, 2022): 5562. http://dx.doi.org/10.11591/ijece.v12i5.pp5562-5572.
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<span>Nowadays, online communication is more convenient and popular than face-to-face conversation. Therefore, people prefer online communication over face-to-face meetings. Enormous people use online chatting systems to speak with their loved ones at any given time throughout the world. People create massive quantities of conversation every second because of their online engagement. People's feelings during the conversation period can be gleaned as useful information from these conversations. Text analysis and conclusion of any material as summarization can be done using sentiment analysis by natural language processing. The use of communication for customer service portals in various e-commerce platforms and crime investigations based on digital evidence is increasing the need for sentiment analysis of a conversation. Other languages, such as English, have well-developed libraries and resources for natural language processing, yet there are few studies conducted on Bangla. It is more challenging to extract sentiments from Bangla conversational data due to the language's grammatical complexity. As a result, it opens vast study opportunities. So, support vector machine, multinomial naïve Bayes, k-nearest neighbors, logistic regression, decision tree, and random forest was used. From the dataset, extracted information was labeled as positive and negative.</span>
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Poushy, Lamisha Haque, Salauddin Ahmed Bhuiyan, Masuma Parvin, Refath Ara Hossain, Nazmun Nessa Moon, Jarin Nooder, and Ashrarfi Mahbuba. "Satisfaction prediction of online education in COVID-19 situation using data mining techniques: Bangladesh perspective." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (October 1, 2022): 5553. http://dx.doi.org/10.11591/ijece.v12i5.pp5553-5561.
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<span>This research focuses on the education-based online learning platform. Due to the <a name="_Hlk109304176"></a>coronavirus disease (COVID-19) epidemic, online education is gaining global popularity. It has shown how successful it is in investigating the quality of online education at the COVID-19 pandemic situation by 799 students from different academic institutions, schools, colleges, and universities. A Google web form has been utilized as the data gathering mechanism for this survey. This paper perused the prediction of online education through data mining and machine learning approaches in an online program. The data was collected through online questionnaires. To predict online education's satisfaction rate, four different types of classifiers are used e.g., logistic regression classifiers, k-nearest neighbors, support vector machine, naive Bayes classifiers. The key purpose of this research is to find out an answer to a question which is, "are the student's satisfied with starting the new online teaching system, or will it be an ambivalent effect for students in the future?".</span>
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Bharathi, M. Chaitanya, Dr A. Seshagiri Rao, B. Sravani, and R. Veeranjaneyulu. "Precinct Vaticinator on Social-Media using Machine Learning Techniques." International Journal of Innovative Research in Computer Science & Technology 10, no. 4 (July 25, 2022): 213–17. http://dx.doi.org/10.55524/ijircst.2022.10.4.26.
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Precinct vaticinator of users from online social media brings considerable research these days. Automatic recognition of precinct related with or referenced in records has been investigated for decades. As a standout amongst the online social network organization, Social-Media has pulled in an extensive number of users who send a millions of tweets on regular schedule. Because of the worldwide inclusion of its users and continuous tweets, precinct vaticinator on Social-Media has increased noteworthy consideration in these days. Tweets, the short and noisy and rich natured texts bring many challenges in research area for researchers. In proposed framework, a general picture of precinct vaticinator using tweets is studied. In particular, tweet precinct is predicted from tweet contents. By outlining tweet content and contexts, it is fundamentally featured that how the issues rely upon these text inputs. In this work, we predict the precinct of user from the tweet text exploiting machine learning techniques namely naïve bayes, Support Vector Machine and Decision Tree.
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Krishna Chaitanya, G., Dinesh Reddy Meka, Vakalapudi Surya Vamsi, and M. V S Ravi Karthik. "A Survey on Twitter Sentimental Analysis with Machine Learning Techniques." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 462. http://dx.doi.org/10.14419/ijet.v7i2.32.16268.
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Sentiment or emotion behind a tweet from Twitter or a post from Facebook can help us answer what opinions or feedback a person has. With the advent of growing user-generated blogs, posts and reviews across various social media and online retails, calls for an understanding of these afore mentioned user data acts as a catalyst in building Recommender systems and drive business plans. User reviews on online retail stores influence buying behavior of customers and thus complements the ever-growing need of sentiment analysis. Machine Learning helps us to read between the lines of tweets by proving us with various algorithms like Naïve Bayes, SVM, etc. Sentiment Analysis uses Machine Learning and Natural Language Processing (NLP) to extract, classify and analyze tweets for sentiments (emotions). There are various packages and frameworks in R and Python that aid in Sentiment Analysis or Text Mining in general.
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Wei, Wei, Tao Peng, Yi Fang, and Li Ye. "The online diagnosis of internal insulation fault of capacitance voltage transformers based on machine learning." Journal of Physics: Conference Series 2290, no. 1 (June 1, 2022): 012025. http://dx.doi.org/10.1088/1742-6596/2290/1/012025.
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Abstract In order to diagnose internal insulation faults of CVT in time, an online diagnosis method for the internal insulation performance of CVT is proposed in this paper. The relationship between CVT insulation performance and measured data is obtained by analyzing CVT internal insulation structure and transfer function. After constructing an appropriate eigenvector by the inter-group correlation amplitude parameter of CVT, three methods of SVM, Bayes, and KNN are used as classifiers for insulation fault diagnosis. The experiment result shows that KNN method can effectively diagnose internal insulation faults with an accuracy rate of 100%.
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Nithisha, T. "Position Forecast on Twitter Using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 218–20. http://dx.doi.org/10.22214/ijraset.2022.45239.
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Abstract: These days, a lot of study is being done on the location predictions of users from online social media. It has been studiedfor decades how to automatically identify locations that is connected to or mentionedin documents. As a leader in the online social network industry, Twitter has attracted a sizable user base that regularly sends millions of tweets. Location prediction onTwitter has gained significant attentionlately as a result of the global reach of its users and the constant stream of posts. Researchers face several difficulties when attempting to do study using tweets, which are brief, noisy, and rich in content. A broadoverview of location prediction using tweets is examined in the suggested framework. In particular, tweet contents are used to forecast tweet location. By describing thecontent and context of tweets, it isfundamentally highlighted how these text inputs play a role in the problems. In this study, we employ naive bayes, support vectormachines, and decision trees as machine learning approaches to predict the user's location from the text of tweets
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Anushaya Prabha, T., T. Aisuwariya, M. Vamsee Krishna Kiran, and Shriram K. Vasudevan. "An Innovative and Implementable Approach for Online Fake News Detection Through Machine Learning." Journal of Computational and Theoretical Nanoscience 17, no. 1 (January 1, 2020): 130–35. http://dx.doi.org/10.1166/jctn.2020.8639.
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One should recollect the USA 2015 and 2016 U.S. presidential election cycle dealt with numerous scandals which were triggered by the forged news articles that blowout through the social media like Twitter and Facebook. When it was found that these articles were purposefully uploaded for financial and political gain, it’s become evident that bogus news has to be identified and removed to prevent public from being deceived for someone’s personal gain. This study builds a supervised machine language model to detect the fake news articles published during 2015 and 2016 U.S. election cycle. The data set contains identical number of bogusand factual news. The standard set of machine learning algorithms like K-Nearest Neighbors, Support Vector Machine, Naive Bayes and Passive Aggressive Classifier are trained using either the title or the content of the article. There results show that the PAC classifier produces the highest accuracy of 94.63% over the other three classifiers using diagram term frequency.
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Kuriakose, Mrs Sony, G. Krishna Teja, A. Harshel Srivatsava, Sravan Duggi, and Venkat Jonnalagadda. "Machine Learning Based Password Strength Analysis." International Journal of Innovative Technology and Exploring Engineering 11, no. 8 (July 30, 2022): 5–8. http://dx.doi.org/10.35940/ijitee.h9119.0711822.
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Passwords, as the most used method of authentication because to its ease of implementation, allow attackers to get access to the accounts owned by others by means of cracking passwords. This is cause of the similar patterns that users use to create a password, like dictionary words, common phrases, person and location names, keyboard pattern, and so on. Multiple password cracking techniques had been introduced to predict the password offline or online, with the majority of records say the one with weak password or familiar password patterns being cracked. This suggested prototype implements numerous machine learning methods such as Decision Tree (DT), Nave Bayes (NB), Logistic Regression (LR), and Random Forest (RF) on a web application in real time to force users to choose a secure password. This results in the user’s account being logged into if particularly the password strength from more than half of the algorithms is strong.
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Kalhotra, Satish Kumar, Shivprasad Vaijnathrao Dongare, A. Kasthuri, and Daljeet Kaur. "Data Mining and Machine Learning Techniques for Credit Card Fraud Detection." ECS Transactions 107, no. 1 (April 24, 2022): 4977–85. http://dx.doi.org/10.1149/10701.4977ecst.
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In the recent era, everybody is dealing with digital data. In such a scenario, individual one heavily depends on credit card. Therefore, the demand of online transactions and usage of e-commerce sites are rising at the rapid rate. The online payments are the main cause of increasing crime rate heavily. Hence, it is the biggest challenge for the IT sector to identify and solve such critical problems. This critical issue can be tackled with the help of machine learning. This paper mainly emphasis on various data mining algorithms such as C4.5, CART algorithms, J48, Naïve Bayes algorithm, EM algorithm, Apriori algorithm, and SVM, and inform the accuracy and precision of the result. The machine learning finds the genuine and non-genuine transition using learning pattern matching and classification technique. The machine learning also normalized the data, identified the anomalies in transaction, and provided appropriate results.
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Sadhasivam, Jayakumar, and Ramesh Babu Kalivaradhan. "Sentiment Analysis of Amazon Products Using Ensemble Machine Learning Algorithm." International Journal of Mathematical, Engineering and Management Sciences 4, no. 2 (April 1, 2019): 508–20. http://dx.doi.org/10.33889/ijmems.2019.4.2-041.
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In recent years, Sentimental Analysis is used in all online product firms. The number of users using the particular product has increased which makes the industry to improvise the characteristics of the product. These days, many users who are using websites, blogs, online shopping tends to review the products they used. These reviews were taken into consideration by other users during their search for products. Hence the industry has found the root of delivering the correct product searched by the user based on the reviews of the users using the concept of sentimental analysis. Sentimental Analysis is the concept of data analysis where the collections of reviews are taken into consideration, and those reviews are analyzed, processed and recommended to the user. The reviews given are longer and which consist of a few paragraphs of content. In this paper, the dataset is collected from the official product sites. Initially, these reviews must be pre-processed in order to remove the unwanted data’s such as stop words, be verbs, punctuations, and conjunctions. Once, the pre-processing is over the trained dataset is classified using Naive Bayes and SVM algorithm. These existing algorithms provided the accuracy which is not worth enough. Hence, an ensemble approach has been applied to enhance the accuracy of the given reviews. An ensemble is a classification approach by combining two or more algorithms and calculate the mode value based on the vote reference for every algorithm which is used. In this paper, Naive Bayes, SVM, and Ensemble algorithm are combined. We proposed an Ensemble method that helps in providing better accuracy than the current existing algorithm. Once the accuracy is calculated, based on the reviews, the particular product is recommended for the user.
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Teixeira, Marcio, Tara Salman, Maede Zolanvari, Raj Jain, Nader Meskin, and Mohammed Samaka. "SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach." Future Internet 10, no. 8 (August 9, 2018): 76. http://dx.doi.org/10.3390/fi10080076.
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This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank’s control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naïve Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments.
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Fattya Ariani and Andi Taufik. "Perbandingan Metode Klasifikasi Data Mining untuk Prediksi Tingkat Kepuasan Pelanggan Telkomsel Prabayar." SATIN - Sains dan Teknologi Informasi 6, no. 2 (December 21, 2020): 46–55. http://dx.doi.org/10.33372/stn.v6i2.666.
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Saat ini Indonesia mempunyai lebih dari satu operator seluler yang aktif. Perkembangan teknologi yang membuat segala aktivitas masyarakat dilakukan secara mobile atau online. Karena kebutuhan komunikasi sangat tinggi maka perusahaan operator saling berlomba-lomba untuk mengambil hati para pelanggan dengan peningkatan layanannya. Telkomsel menjadi salah satu operator terbesar di Indonesia. Untuk memberikan pelayanan terbaik, telkomsel harus konsisten dan meningkatkan pelayanan. Ada beberapa metode yang dapat digunakan untuk menganalisa klasifikasi kepuasan pelanggan seperti Support Vector Machine, Decission Tree dan Metode Naïve Bayes dan PSO. Tetapi belum diketahui tingkat akurasi paling tinggi dalam mengklasisfikasi kepuasan pelanggan. Untuk mengetahui tingkat akurasi tertinggi diantara metode tersebut, maka dilakukan penelitian perbandingan metode klasifikasi data mining untuk prediksi tingkat kepuasan pelanggan telkomsel prabayar. Penelitian dilakukan dengan penyebaran kuesioner dengan jumlah 500 responden. Dan variabel yang dinilai ada 4 yaitu harga, promosi, kualitas produk dan kualitas layanan. Hasil dari penelitian ini di dapatkan nilai akurasi algoritma C45 sebesar sebesar 96,50%, Support Vector Machine sebesar 89,66%, Naïve Bayes sebesar 89,88% dan metode Optimasi Naïve Bayes dengan pemilihan fiture Particle Swarm Optimization sebesar 95,85%. Jadi algoritma C.45 nilai akurasinya paling tinggi dibandingkan dengan metode lainnya. Sehingga menggunakan algoritma C.45 lebih baik dalam memprediksi tingkat kepuasan pelanggan telkomsel prabayar
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Uma Ramya, V., and K. Thirupathi Rao. "Sentiment Analysis of Movie Review using Machine Learning Techniques." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 676. http://dx.doi.org/10.14419/ijet.v7i2.7.10921.
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Today's online world was fully filled up with blogs, views, comments, posts through various websites and social-surfs. People were habituated with posting every incident into blogs, messed with comments like text and emotions, which are a mixed bag of sad, happy, worry, cry etc. Analysing such data was called as Sentimental Analysis. To analysis, these unordered data we use new emerged technology algorithms. Machine learning a transpire technology which is engaged with almost all the fields, where its algorithms are more powerful that give with better faultless results. In this paper, we are analyzing tweets based on movie reviews using the Multinomial Logistic Regression, Naïve Bayes, and SVM algorithms to compare score value to show the best text analysis algorithm.
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ÇELİK, Rüveyda, and Ali GEZER. "Detection of Trickbot and Emotet Banking Trojans with Machine Learning." Balkan Journal of Electrical and Computer Engineering 10, no. 4 (October 1, 2022): 377–87. http://dx.doi.org/10.17694/bajece.1031021.
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Internet banking is getting more popular with the increasing number and demand of online banking customers. Almost all transactions that could be performed in bank branches could also be realized through internet banking. Internet banking, which has become widespread with the increasing use of the Internet, has also led to an increase in cases of financial fraud. This has made the protection of personal data and the security of banking services more important than ever. It is very important for institutions and organizations providing online banking services to take security measures in their systems. Cybercriminals target internet users with methods such as malware infection, botnets, spam, phishing, identity theft, and social engineering that they use and develop every day. Therefore, there are always potential risks in using internet banking. Banking viruses commonly used by cybercriminals today are TrickBot and Emotet. Nowadays TrickBot and Emotet are popular banking trojans which gives hard times for online banking customers. Their primary goal is to steal user’s banking and personal information. In this study, we will investigate the behavior analysis and new tricks of TrickBot and Emotet banking viruses, which use different methods to compromise the security of online banking customers. We benefited WEKA program to detect these banking viruses. In addition to this, we also focused on the detection of TrickBot and Emotet Banking viruses with using Random Tree, J48, Naive Bayes, SMO Techniques.
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TIWARI, ANKIT, and Dr BRAJESH KUMAR SINGH. "Using Navies Bayes Algorithm InSentimental Analysis To Improve Business Intelligence." Journal of University of Shanghai for Science and Technology 23, no. 10 (October 9, 2021): 391–97. http://dx.doi.org/10.51201/jusst/21/10720.
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A study of sentimental analysis is opinion mining, feeling, emotions, sentiments mining and sentiments extraction has increased its acceptance some year ago. Now a Days Online survey reviews have become very essential criteria for checking the status of a company. Now In, This research paper we represent a sentimental analysis method to company reviews organization through extensive reviews datasets which are given by Yelp, Yelp Challenging datasets. In the search paper, we represent many techniques for automatic sentimental analysis classification, by using two methods for extraction methods and by using four techniques in machine learning models. It reflects the similar research on the influence of the ensemble techniques for reviewing the sentimental analysis classification.
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Abo, Mohamed Elhag Mohamed, Norisma Idris, Rohana Mahmud, Atika Qazi, Ibrahim Abaker Targio Hashem, Jaafar Zubairu Maitama, Usman Naseem, Shah Khalid Khan, and Shuiqing Yang. "A Multi-Criteria Approach for Arabic Dialect Sentiment Analysis for Online Reviews: Exploiting Optimal Machine Learning Algorithm Selection." Sustainability 13, no. 18 (September 7, 2021): 10018. http://dx.doi.org/10.3390/su131810018.
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A sentiment analysis of Arabic texts is an important task in many commercial applications such as Twitter. This study introduces a multi-criteria method to empirically assess and rank classifiers for Arabic sentiment analysis. Prominent machine learning algorithms were deployed to build classification models for Arabic sentiment analysis classifiers. Moreover, an assessment of the top five machine learning classifiers’ performances measures was discussed to rank the performance of the classifier. We integrated the top five ranking methods with evaluation metrics of machine learning classifiers such as accuracy, recall, precision, F-measure, CPU Time, classification error, and area under the curve (AUC). The method was tested using Saudi Arabic product reviews to compare five popular classifiers. Our results suggest that deep learning and support vector machine (SVM) classifiers perform best with accuracy 85.25%, 82.30%; precision 85.30, 83.87%; recall 88.41%, 83.89; F-measure 86.81, 83.87%; classification error 14.75, 17.70; and AUC 0.93, 0.90, respectively. They outperform decision trees, K-nearest neighbours (K-NN), and Naïve Bayes classifiers.
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Rintyarna, Bagus Setya. "Joint Distribution pada Weighted Majority Vote (WMV) untuk Peningkatan Kinerja Sentiment Analysis Tersupervisi pada Dataset Twitter." Jurnal Teknologi Informasi dan Ilmu Komputer 9, no. 5 (October 31, 2022): 1083. http://dx.doi.org/10.25126/jtiik.2022956185.
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<p class="Abstrak"><em>Sentiment analysis</em> adalah teknik komputasi <em>text mining</em> berbasis <em>natural language processing</em> (NLP) untuk mengekstraksi pendapat seseorang yang diungkapkan dalam platform online, termasuk dalam platform <em>microblogging</em> Twitter, salah satu platform <em>microblogging</em> yang paling popular digunakan di Indonesia. Ada dua pendekatan yang umum digunakan dalam teknik sentiment analysis yaitu pendekatan berbasis <em>machine learning</em> (ML) dan pendekatan berbasis <em>sentiment lexicon</em> (SL). Fokus penelitian ini adalah untuk pengembangan teknik <em>sentiment analysis</em> berbasis <em>machine learning</em> yang disebut juga teknik tersupervisi pada dataset Twitter. Sebagian besar sentiment analysis pada dataset Twitter berbahasa Indonesia mengandalkan <em>single machine learning algorithm</em>. Penelitian ini menggabungkan kinerja berbagai algoritma/experts seraya mengurangi tingkat kesalahan klasifikasi dengan meng-update bobot secara dinamis menggunakan <em>weighted majority vote</em> (WMV) berbasis <em>joint distribution</em> dari Bayesian Network. Pada tahap pertama, data di grabbing dari Twitter dengan 3 hashtag terkait Covid-19 sebagai data eksperimen. Selanjutnya kinerja weighted majority vote secara ekstensif dibandingkan dengan 4 metode baseline sebagai pembanding, yaitu: Naïve Bayes, Gaussian Naïve Bayes, Multinomial Naïve Bayes dan Majority Vote dari ketiga single classifier tersebut. Metrics kinerja yang digunakan adalah precision, recall, fmeasure, accuracy dan Mathews correlation coeficient (MCCC). Dalam eksperimen, terbukti bahwa WMV mampu meningkatkan kinerja <em>sentiment analysis</em> pada ketiga topik dataset dengan evaluator berbagai metrics kinerja sentiment analysis.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Sentiment analysis is a computational text mining technique based on natural language processing (NLP) to extract someone's opinion expressed in online platforms, including the Twitter microblogging platform, one of the most popular microblogging platforms used in Indonesia. There are two approaches that are commonly used in sentiment analysis techniques, namely the machine learning (ML) based approach and the sentiment lexicon (SL) based approach. The focus of this research is the development of machine learning-based sentiment analysis techniques which are also called supervised techniques on the Twitter dataset. Most of the sentiment analysis on the Indonesian language Twitter dataset relies on a single machine learning algorithm. This study combines the performance of various algorithms/experts while reducing the level of misclassification by updating the weights dynamically using a joint distribution-based weighted majority vote (WMV) from the Bayesian Network. In the first stage, data was grabbed from Twitter with 3 hashtags related to Covid-19 as experimental data. Furthermore, the performance of the weighted majority vote was extensively compared with 4 baseline methods for comparison, namely: Naïve Bayes, Gaussian Naïve Bayes, Multinomial Nave Bayes and Majority Vote from the three single classifiers. Performance metrics used are precision, recall, fmeasure, accuracy and Mathews correlation coeficient. In experiments, it is proven that WMV is able to improve sentiment analysis performance on the three dataset topics with various evaluators of sentiment analysis performance metrics.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>
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Shukla, Rachit, Adwitiya Sinha, and Ankit Chaudhary. "TweezBot: An AI-Driven Online Media Bot Identification Algorithm for Twitter Social Networks." Electronics 11, no. 5 (February 28, 2022): 743. http://dx.doi.org/10.3390/electronics11050743.
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In the ultra-connected age of information, online social media platforms have become an indispensable part of our daily routines. Recently, this online public space is getting largely occupied by suspicious and manipulative social media bots. Such automated deceptive bots often attempt to distort ground realities and manipulate global trends, thus creating astroturfing attacks on the social media online portals. Moreover, these bots often tend to participate in duplicitous activities, including promotion of hidden agendas and indulgence in biased propagation meant for personal gain or scams. Thus, online bots have eventually become one of the biggest menaces for social media platforms. Therefore, we have proposed an AI-driven social media bot identification framework, namely TweezBot, which can identify fraudulent Twitter bots. The proposed bot detection method analyzes Twitter-specific user profiles having essential profile-centric features and several activity-centric characteristics. We have constructed a set of filtering criteria and devised an exhaustive bag of words for performing language-based processing. In order to substantiate our research, we have performed a comparative study of our model with the existing benchmark classifiers, such as Support Vector Machine, Categorical Naïve Bayes, Bernoulli Naïve Bayes, Multilayer Perceptron, Decision Trees, Random Forest and other automation identifiers.
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Chan, Timothy. "Predictive Models for Determining If and When to Display Online Lead Forms." Proceedings of the AAAI Conference on Artificial Intelligence 28, no. 2 (July 27, 2014): 2882–89. http://dx.doi.org/10.1609/aaai.v28i2.19010.
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This paper will demonstrate a machine learning application for predicting positive lead conversion events on the Edmunds.com website, an American destination for car shopping. A positive conversion event occurs when a user fills out and submits a lead form interstitial. We used machine learning to identify which users might want to fill out lead forms, and where in their sessions to present the interstitials. There are several factors that make these predictions difficult, such as (a) far more negative than positive responses (b) seasonality effects due to car sales events near holidays, which require the model to be easily tunable and (c) the need for computationally fast predictions for real-time decision-making in order to minimize any impact on the website’s usability. Rather than develop a single highly complex model, we used an ensemble of three simple models: Naive Bayes, Markov Chain, and Vowpal Wabbit. The ensemble generated significant lift over random predictions and demonstrated comparable accuracy to an external consulting company’s model.