Weighted naïve Bayes text classification algorithm based on improved distance correlation coefficient

With an ever-increasing number of mobile users, the development of mobile applications (apps) has become a potential market during the past decade. Billions of users download mobile apps for divergent use from Google Play Store, fulfill tasks and leave comments about their experience. Such reviews are replete with a variety of feedback that serves as a guide for the improvement of existing apps and intuition for novel mobile apps. However, application reviews are challenging and very broad to approach. Such reviews, when segregated into different classes guide the user in the selection of suitable apps. This study proposes a framework for analyzing the sentiment of reviews for apps of eight different categories like shopping, sports, casual, etc. A large dataset is scrapped comprising 251661 user reviews with the help of ‘Regular Expression’ and ‘Beautiful Soup’. The framework follows the use of different machine learning models along with the term frequency-inverse document frequency (TF-IDF) for feature extraction. Extensive experiments are performed using preprocessing steps, as well as, the stats feature of app reviews to evaluate the performance of the models. Results indicate that combining the stats feature with TF-IDF shows better performance and the support vector machine obtains the highest accuracy. Experimental results can potentially be used by other researchers to select appropriate models for the analysis of app reviews. In addition, the provided dataset is large, diverse, and balanced with eight categories and 59 app reviews and provides the opportunity to analyze reviews using state-of-the-art approaches.

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“…Applications of MNB include text classification [ 42 ], apps review [ 11 ], sentiment classification [ 32 ], and so on.…”

Section: Proposed Methodologymentioning

confidence: 99%

Performance evaluation of machine learning models on large dataset of android applications reviews

Qureshi

Ahmad

Ullah

et al. 2023

Multimed Tools Appl

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“…Supervised methods usually use statistical methods to evaluate each category and use classification functions to select the category with the highest probability as the result. Representative methods include naïve Bayes [6,7], logistic regression [8], decision tree [9], deep neural networks [10][11][12], and so on.…”

Section: Text Classification Methodsmentioning

confidence: 99%

Ptr4BERT: Automatic Semisupervised Chinese Government Message Text Classification Method Based on Transformer-Based Pointer Generator Network

Yin

Wang

2022

Advances in Multimedia

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With the development of Internet technology, government affairs can be handled online. More and more citizens are using online platforms to report to government departments, which is generating a lot of textual data. Among them, the basic but important problem is to automatically classify the different categories of messages, so that staff from different departments can process relevant information quickly. However, government messages have problems such as fast update rate, a large amount of information, long texts, and difficulty in capturing key points, which make supervised learning methods unsuitable for processing such texts. To address these problems, we propose a semisupervised text classification method based on a transformer-based pointer generator network named Ptr4BERT, which uses the pointer generator network with BERT(bidirectional encoder representation from transformers) embedding as a preprocessor for feature extraction. In this method, text classification can achieve very good results with a small set of labeled data, by extracting features exclusively from the message text. In order to verify the effect of our proposed model, we performed some experiments. Besides, we designed a crawler program and obtained two datasets from different websites, which are named HNMes and QDMes. Experimental results have shown that the proposed method outperforms the state-of-the-art methods significantly.

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“…Naive–Bayes utilizes Bayesian theory that employs the training samples to predict the sort of unidentified samples based on previous results. The model of Bayesian classification is based on statistical assessment and the concept of Bayesian, formed for Bayesian learning [ 54 , 55 ]. Bayesian learning combines the preceding and subsequent probability and utilizes it to determine the later probability according to the information and data samples provided.…”

Section: Background and Related Workmentioning

confidence: 99%

Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

Prabakaran

Ramalakshmi

Hussain

et al. 2022

Sensors

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Decoupled data and control planes in Software Defined Networks (SDN) allow them to handle an increasing number of threats by limiting harmful network links at the switching stage. As storage, high-end servers, and network devices, Network Function Virtualization (NFV) is designed to replace purpose-built network elements with VNFs (Virtualized Network Functions). A Software Defined Network Function Virtualization (SDNFV) network is designed in this paper to boost network performance. Stateful firewall services are deployed as VNFs in the SDN network in this article to offer security and boost network scalability. The SDN controller’s role is to develop a set of guidelines and rules to avoid hazardous network connectivity. Intruder assaults that employ numerous socket addresses cannot be adequately protected by these strategies. Machine learning algorithms are trained using traditional network threat intelligence data to identify potentially malicious linkages and probable attack targets. Based on conventional network data (DT), Bayesian Network (BayesNet), Naive-Bayes, C4.5, and Decision Table (DT) algorithms are used to predict the target host that will be attacked. The experimental results shows that the Bayesian Network algorithm achieved an average prediction accuracy of 92.87%, Native–Bayes Algorithm achieved an average prediction accuracy of 87.81%, C4.5 Algorithm achieved an average prediction accuracy of 84.92%, and the Decision Tree algorithm achieved an average prediction accuracy of 83.18%. There were 451 k login attempts from 178 different countries, with over 70 k source IP addresses and 40 k source port addresses recorded in a large dataset from nine honeypot servers.

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Weighted naïve Bayes text classification algorithm based on improved distance correlation coefficient

Cited by 19 publications

References 22 publications

Performance evaluation of machine learning models on large dataset of android applications reviews

Performance evaluation of machine learning models on large dataset of android applications reviews

Ptr4BERT: Automatic Semisupervised Chinese Government Message Text Classification Method Based on Transformer-Based Pointer Generator Network

Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

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