Semi-supervised Research Papers

Sentiment Analysis of Online Shopping Platform Product Reviews

2025, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

Sentiment analysis has become a very important tool for understanding customer sentiments, brand perception, and market trends from text data. This project is centered on conducting sentiment analysis of Amazon product review datasets... more

Sentiment analysis has become a very important tool for understanding customer sentiments, brand perception, and market trends from text data. This project is centered on conducting sentiment analysis of Amazon product review datasets using machine learning classifiers and optimization methods for improving the accuracy of predictions. In the current work, we utilize two main models: Random Forest Classifier and a Decision Tree Classifier optimized using Grey Wolf Optimization (GWO). The dataset used contains product reviews, from which the review text and their corresponding scores were extracted. Reviews were preprocessed by lowercasing text, stripping of punctuation, and stopwords removal to achieve cleaner inputs for models. A binary classification approach was taken, assigning a positive label to reviews with a score higher than three, and a negative label to those with a score three and lower. In order to transform the text data into a machine learning algorithm-friendly format, the TF-IDF (Term Frequency-Inverse Document Frequency) method was used, which captures the significance of words in relation to the dataset. The Random Forest Classifier, an ensemble learning algorithm that builds multiple decision trees and returns the mode of their predictions, was the first baseline model. It showed strong performance because it could minimize overfitting and deal with highdimensional data well. To investigate optimization methods further, a Decision Tree Classifier was trained whose hyperparameters, namely the maximum depth, were optimized using Grey Wolf Optimization. GWO, which is a nature-inspired metaheuristic algorithm based on the hunting behavior of grey wolves, is a simulation of the leadership structure and cooperative hunting approach of wolves to discover optimal solutions. Our findings indicated that the Random Forest model had high classification accuracy with little tuning. Yet, the Decision Tree model, when optimized through GWO, proved to be competitive, highlighting the power of metaheuristic optimization methods in improving conventional machine learning models. Confusion matrices and classification reports were used to give deeper insights into the precision, recall, and F1-scores of each model. This investigation showcases the effectiveness of using traditional classifiers in conjunction with intelligent optimization algorithms for sentiment classification tasks. It further highlights the criticality of preprocessing steps and feature extraction methods such as TF-IDF in deciding the overall efficacy of the models. Although the present work was mostly concerned with the optimization of one hyperparameter through GWO, future research could investigate multi-parameter optimization and comparison with other swarm intelligence algorithms such as Particle Swarm Optimization (PSO) or Genetic Algorithms (GA) in order to enhance performance further. In general, the project is a holistic method of addressing sentiment analysis on real product review data using a combination of ensemble approaches, decision trees, and evolutionary optimization.. I.

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E-Mail Spam Detection using Machine Learning and Deep Learning

by Shivam Pandey

2025, International Journal for Research in Applied Science and Engineering Technology

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An Intent Prediction System for Wireless Fidelity (Wi-Fi) Sensor Data Using an Emerging Neural Machine Intelligence

by IJRASET Publication

2025, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

This paper presents a User Intent (UI) mining scheme based on an emerging neural machine intelligence technique called the Neuronal Auditory Machine Intelligence (NeuroAMI) and considering a Wi-Fi sessions dataset containing about 8000... more

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EPLSC: A New Semi-Supervised Ensemble Spectral Clustering Algorithm Based on The Graph P-Laplacian for Genetic Data

by Advances in Engineering and Intelligence Systems (AEIS)

2025, Advances in Engineering and Intelligence Systems

Due to the ever-increasing amount of information and their detailed analysis, the problem of clustering, which is used to reveal hidden patterns in data, is still of great importance. On the other hand, the clustering of important genetic... more

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A novel semi-supervised consensus fuzzy clustering method for multi-view relational data

by International Journal of Electrical and Computer Engineering (IJECE) and

2024, International Journal of Electrical and Computer Engineering (IJECE)

Multi-view data is widely employed in various domains, highlighting the need for advanced clustering methodologies to efficiently extract knowledge from these datasets. Consequently, multi-view clustering has emerged as a prominent... more

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Application Of Machine Learning Techniques For Fake Customer Review Detection

by Sushma Vispute

2024, Asian journal of convergence in technology

Now-a-days with the increasing demand of the web , online marketing is additionally becoming progressively popular. This is often because; tons of products and services are easily available online. Hence, reviews of these products and... more

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E-Mail Spam Detection using Machine Learning and Deep Learning

by Shivam Pandey

2024, International Journal for Research in Applied Science and Engineering Technology

Here we present an inclusive review of recent and successful content-based e-mail spam filtering techniques. Our focus is primarily on machine learning-based spam filters and variants that are inspired by them. We report on related ideas,... more

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Identifying Deceptive Opinion Spam using Aspect-based Emotions and Human Behavior Modeling

by Aditi Sharan

2024, International Journal of Hybrid Information Technology

Online product reviews have become the major source of information for the end users to make purchasing decisions. Companies/individuals often hire people for writing fake reviews to increase the sale of their products. These individuals... more

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Online Examination System

by Anshika Shukla

2024, International Journal for Research in Applied Science and Engineering Technology

The online Examination portal is a web-based application for technical evaluation. The online examination portal not only replaces paperwork but also releases the workload of faculty. Most of the e-examination (Online Examinations) have... more

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Social Media Spam Comments Detection Analysis using Machine Learning

by Vaibhav Ambhore

2024, International Journal for Research in Applied Science and Engineering Technology

The results published by Google's famous video distribution platform Social Media are attracting more and more users every day. However, this success has also attracted many malicious users who try to promote their videos or infect them... more

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A Study on Opinion Spamming: Fake Consumer Review Detection

by Dr. Manish Madhava Tripathi

2024, Journal of Informatics Electrical and Electronics Engineering (JIEEE)

Online audits are the most important wellsprings of data about client feelings and are considered the columns on which the standing of an association is assembled. From a client's viewpoint, audit data is vital to settle on an... more

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A Study on Opinion Spamming: Fake Consumer Review Detection

by Dr Manish Madhava Tripathi

2023, Journal of Informatics Electrical and Electronics Engineering (JIEEE)

Online audits are the most important wellsprings of data about client feelings and are considered the columns on which the standing of an association is assembled. From a client's viewpoint, audit data is vital to settle on an... more

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Opinion Mining of Amazon Data for Alexa Review Analysis

by Neetima Gautam

2023, International Journal for Research in Applied Science and Engineering Technology

The fast expansion in piles of unstructured literary information joined by multiplication of devices to investigate them has opened up extraordinary freedoms and difficulties for text mining research. The programmed naming of information... more

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Fraud Detection on Social Media using Data Analytics

by Surbhi Singh

2023, International Journal of Engineering Research and

Data Analytics is one of the newest and emerging technologies. An application of artificial intelligence (AI), data analytics provides the ability to learn and improve systems without being programmed but from experience automatically. It... more

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Identifying Fake News via Machine Learning and Web Scrapping

by Devanshu Rathi

2023, International Journal for Research in Applied Science and Engineering Technology

After digitalization, the increase in use of social media has led the flow of information among the social network users unchecked. The news is immediately transported to social networks, where it is quickly read, marked with opinions (on... more

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Online Fake Review Detection Based on Machine Learning Techniques

by Pratiksha Kale

2023, International Journal of Scientific Research in Science, Engineering and Technology

In E-Commerce client’s reviews can assume a huge part in deciding income of an association. As the vast majority of individuals require review about an item prior to spending their cash on that item. So individuals went over different... more

Figure 1. Aspect Based Sentiment Analysis

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Defining and Auto-Detection of Fake News-Classifier

by Nikhil Mishra

2023, Samvakti Journal of Research in Information Technology

The rise of fake news is a major concern to the society as media being influenced with wrong propaganda affecting the people negatively by making them believe wrong facts about an institution or organization causing harm to the image of... more

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A Batch Based Approach for Tweeting Geotags of Social Media Attributes

by IJRASET Publication

2023, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

Online evaluation systems play an vital position in influencing client conduct and choice making, attracting many spammers to insert faux remarks to review content material and rankings. To increase advantages and improve user experience,... more

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Fake Detection of Online Reviews using Semi-Supervised and Supervised Learning

by Pavan Naik

2023

Online reviews have great impact on today's business and commerce. Decision making for purchase of online products mostly depends on reviews given by the users. Hence, opportunistic individuals or groups try to manipulate product... more

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Online Fake Review Detection Based on Machine Learning Techniques

by International Journal of Scientific Research in Science, Engineering and Technology IJSRSET

2023, International Journal of Scientific Research in Science, Engineering and Technology

In E-Commerce client’s reviews can assume a huge part in deciding income of an association. As the vast majority of individuals require review about an item prior to spending their cash on that item. So individuals went over different... more

In E-Commerce client’s reviews can assume a huge part in deciding income of an association. As the vast majority of individuals require review about an item prior to spending their cash on that item. So individuals went over different audits in the site however these surveys are genuine or counterfeit isn’t distinguished by the user .In survey sites some great review are added by the item organization individuals itself to create bogus positive item review. They give great surveys for some, various items fabricated by their own firm. Client will not ready to see if the survey is genuine or counterfeit. The suggestion motor creates benefits dependent on client profiles and previous authentic studying movement for clients who have as of late joined the framework and unequivocally permitted web history. Consolidate the data separating strategy with the client profiles gained from the present community sifting procedure to give customized audit proposals. The proposed concentrate on utilizes a mixture AI framework to suggest web surveys. The framework first works utilizing Natural Language Processing (NLP) to extricate elements and train the module. The technique might direct investigations dependent on the client’s very own set of experiences. We recommend an item viewpoint surveys system in this paper, featuring fundamental components of items to expand the convenience of the various assessments. For example, given an item’s client reviews, we utilize a feeling classifier to identify item attributes and decide shopper assessments on these components. Then, at that point, utilizing a concurrent thought of viewpoint recurrence and the impact of client surveys given to every perspective over their unfit feelings, we foster an angle positioning calculation to deduce the importance of perspectives. We then, at that point, gauge these variables to get the item’s general grade. The proposed outfit model beats a few current methodologies, thus giving a clever answer for handle information lop-sidedness and component pruning troubles in the space of phony survey ID.

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Fraud Detection on Social Media using Data Analytics

by Surbhi Singh

2023, International Journal of Engineering Research and

Data Analytics is one of the newest and emerging technologies. An application of artificial intelligence (AI), data analytics provides the ability to learn and improve systems without being programmed but from experience automatically. It... more

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Defining and Auto-Detection of Fake News-Classifier

by Siddharth Nanda

2023, Samvakti Journal of Research in Information Technology

The rise of fake news is a major concern to the society as media being influenced with wrong propaganda affecting the people negatively by making them believe wrong facts about an institution or organization causing harm to the image of... more

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Text Mining in Hotel Reviews: Impact of Words Restriction in Text Classification

by Rodrigo Rocha Silva

2023, Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Text Mining is the process of extracting interesting and non-trivial patterns or knowledge from unstructured text documents. Hotel Reviews are used by hotels to verify client satisfaction regarding their own services or facilities.... more

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E-Mail Spam Detection using Machine Learning and Deep Learning

by shivam pandey

2023, International Journal for Research in Applied Science and Engineering Technology

Here we present an inclusive review of recent and successful content-based e-mail spam filtering techniques. Our focus is primarily on machine learning-based spam filters and variants that are inspired by them. We report on related ideas,... more

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Data Analyzation using Regression (Sales Prediction

by IJRASET Publication

2023, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

This model is made for overcoming the excessive production of any product that occurs by inappropriate analysis of data. Sometimes the requirement is more but product shortage always leads companies to suffer from various types of... more

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Legal Document Analysis

by IJRASET Publication

2023, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

Text preprocessing is the most essential and foremost step for any Machine Learning model. The raw data needs to be cleaned and pre-processed to get better performance. It is the method to clean the data and makes it ready to feed the... more

Fig 4.4.2 Visualization of words with the number of times they've appeared in the corpus

Fig 4.4.4 Pie chart showing the count of the type of contracts in the dataset

E. LSTM This deep learning model gave an accuracy of 99.95%. Fig 5.5.1 Accuracy of LSTM for proposed system

Fig 5.2.5a Accuracy of unigram and bigram hybrid using Logistic Regression The hybrid model of unigram and bigram gave an accuracy of 92.15%.

Fig 5.2.2a Accuracy of unigram model for the proposed system The Bigram model using the Logistic Regression Classifier gave an accuracy of 94.11%. The Unigram model using Logistic Regression classifier gave an accuracy of 86.92%.

Fig 5.2.3a Accuracy of unigram model for the proposed system The Trigram model using Logistic Regression gave an accuracy of 66.66%.

Fig 5.2.4a Accuracy of trigram model for the proposed system

Fig 5.2.5a Accuracy of bigram and trigram hybrid using Logistic Regression Fig 5.2.5a Accuracy of unigram, bigram and trigram hybrid using Logistic Regression Fig 5.3.1 Accuracy and classification report of OneVsRestClassifier using Logistic Regressior

Fig 5.4.2a Accuracy of the blending algorithm Fig 5.4.2b Classification report of the blending model After blending logistic regression, KNeighbours Classifier, Decision tree Classifier, Support Vector Machine Classifier, Naive Bayes Classifier and OneVsRest Classifier gave an accuracy of 99.408%.

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Fraud Apps Detection Using Sentiment Analysis and Spam Filtering

by IJRASET Publication

2023, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

In the mobile app industry, ranking fraud is the practice of engaging in dishonest or deceitful behavior with the intention of artificially boosting an App's position on a popularity list. In fact, ranking fraud by app developers is... more

The paper study can be summarized into a table which can be grouped into various categories for analysing whether the app is fraudulent or not.

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A Comprehensive Overview on Intelligent Spam Email Detection

by IJRASET Publication

2023, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

Spam, usually referred to as unsolicited commercial or bulk e-mail, has recently become a major issue on the internet. Time, storage, and transmission bandwidth are all wasted by spam. Spam email has been a growing issue for years.... more

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Comprehensive Survey of Different Techniques and Methods Used for Eshop Software Application

by IJRASET Publication and

2023, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

Latest improvements in the online shopping system through the machine learning, data mining to improve the handcraft products marketing. One of the main reason to build the solutions for the handcrafts is to develop the products with the... more

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Virtual Try on Clothes

by Reshma Kale

2023, International Journal for Research in Applied Science and Engineering Technology

Trying on different clothes in the store and finally choosing the right one can be a time-consuming and tedious process. So, a real-time virtual fitting room is a concept where a customer can buy pieces of her clothes without having to... more

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Spam Detection by Using KNN Algorithm Techniques

by nikita deshmukh

2023

Abstract: Preceding purchasing an item, individuals typically advise themselves by perusing on the web reviews. To make more benefit dealers frequently attempt to fake client experience. As clients are being misdirected along these lines,... more

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Spam Mail Classifier

by IJRASET Publication

2022, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

Email is the worldwide use of communication application. It is because of the ease of use and faster than other communication application. However, its inability to detect whether the mail content is either spam or ham degrade its... more

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Performance and Feedback Analyzer

by IJRASET Publication

2022, International Journal for Research in Applied Science and Engineering Technology (IJRASET)

We examine opinion mining using supervised learning techniques to discover the sentiment of student inputs supported by labeled teaching and learning decisions. The exams conducted included undergraduate input data collected from VR... more

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Fake News Detection Using Deep Learning

by IJRASET Publication

2022, International Journal for Research in Applied Science and Engineering Technology (IJRASET)

Social media news may be a double-edged sword. There are a number of benefits to utilizing it: It's simple to use, takes little time, and is user-friendly. It's also simple to share socially significant data with others. On the other... more

Figure 1. False news on social media: from recognition to detection.[3] Authenticity and aim are two aspects of the concept of fake news. Authenticity entails the verification of falsifiable information, which implies that the conspiracy theory is not included in the falsified news since it is either false or true in most circumstances. The document's purpose, the second component, consists of writing incorrect facts in order to fool the reader.

Linear regression and logistic regression patterns fail when characteristics and outcomes are non-linear or interact with one another. Now is your chance to shine in the decision tree! Data is multiplied by particular cut-offs in functions in tree-based models. A sub- set per instance is used to create different sub-sets. End nodes or feature nodes relate to the last subsets, whereas internal nodes or splits refer to the secondary subsets. To forecast the outcome, the average training results for each node are used. Classification and regression may both be done with trees. 1) Advantages

F. Add titles to Colomns and Merge Them H. Filter the fake and true news by applying Various Processes.

L. Apply Gradient Boosting and Check the Results.

After applying all these algorithms, it was found that Gradient Boost algorithm performed the best followed by Decision Tree algorithm.

N. Apply Naive Bayes and Check the Results.

With more people using the internet, spreading false information is becoming easier. Many people use the Internet and social media on a regular basis. On these sites, there are no limits on posting news. As a result, some people take advantage of these channels and start disseminating false information about people or organizations. This might ruin a person's reputation or have an impact on a business. False news can also influence public opinion about a political party. This fake news must be discovered. We used five different machine learning algorithms and found Gradient Boost to be the best.

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Defining and Auto-Detection of Fake News-Classifier

by Nikhil Mishra

2022, Samvakti Journal of Research in Information Technology

The rise of fake news is a major concern to the society as media being influenced with wrong propaganda affecting the people negatively by making them believe wrong facts about an institution or organization causing harm to the image of... more

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Identifying Deceptive Opinion Spam using Aspect-based Emotions and Human Behavior Modeling

by mayank saini

2022, International Journal of Hybrid Information Technology

Online product reviews have become the major source of information for the end users to make purchasing decisions. Companies/individuals often hire people for writing fake reviews to increase the sale of their products. These individuals... more

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Online Spam Review Detection: A Survey of Literature

by Guandong Xu

2022, Human-Centric Intelligent Systems

The increasingly developed online platform generates a large amount of online reviews every moment, e.g., Yelp and Amazon. Consumers gradually develop the habit of reading previous reviews before making a decision of buying or choosing... more

Fig. 1 Distribution of Focused Research Works. Review Mining Task takes the percentage of 17%, end-to-end classification task is 22%, and cold-start problems have the same proportion as classification task. Spammer detection task takes the percentage of 39%

Fig. 2 Wang et al., model overview. They take the products items as the head part of the TransE network in their model, take the reviewers as the translation (relation) part and take the review as the tail part

Fig. 3 Li et al., model overview. The embedding network consists four parts: user embedding layers, item embedding layers, review embedding networks, and rating embedding layers

Fig.4 Xie et al., model overview. Relationship of Spammers. Reviewers and Customers

Fig.5 Yang et al., WMUSVM Algorithm Overview. This demon- strate builds up hypersphere with greatest volume, containing all beguiling reviews information, and all genuine reviews information are exterior of this hypersphere

Fig.6 Nayak et al., review indicator framework. They demonstrate the review spamicity expectation as a two-class classification issue. The review spamicity pointer was built utilizing stacked LSTM mod- els and show yields the individual probabilities of each review being genuine or fakes

Table 1 Description of features in CATS Multicriteria Decision: Viviani et al. [20] used a multicrite- ria choice making approach based both on the evaluation of numerous criteria and the utilize of accumulation adminis- trators with the point of getting a veracity score related with each review. Based on this score, it is conceivable to identify spam reviews [20]. Specifically, they provide a definition of aggregation operator F:

Table 4 Op_spam_v1.4 Datasets statistics

“Industry manufactured products like electronics, computers, etc. Table 5 Various features of different categories of products

Table 7 Statistics of the 500 restaurants in Shanghai

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Detection of Fake Online Reviews using Semisupervised and Supervised learning

by IJRASET Publication

2022, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

Nowadays, when somebody wants to make some decisions about a product or a service everyone goes with the reviews as it has become an essential part of decision making. When a customer wants to order a product on an e commerce website... more

The figure lrepresents the proposed system architecture. The proposed system principally focuses on machine learning techniques. With the thought of the present challenges in fake reviews classification making an attempt to enhance accuracy mistreatment completely different classifiers. Using RF, SVM to get accurate results to classify the fake reviews. Both Random Forest RF and SVM classifiers offer correct results. The preprocessing of the data sets is incredibly vital in classification. The feature extraction and choice leads to the potency of the system. The american news dataset were used for training and testing. The planned model has its own benefits that it's simpler as compared to existing systems.

Figure 3.2. Detailed design of the proposed model

ed BACENUMEUETE FUE OE Random forest may be a supervised classification algorithmic program. RF trees also are referred to as as Random decision forests. it's used for classification, regression. It depends on the quantity of trees that exist. If there are a larger range of trees the a lot of is that the accuracy. during this the foundation node and have node split are done every randomly. The random forest creation and prediction are the 2 steps concerned during this algorithmic program. It operates by constructing a decision tree at training time and ultimately it labels the category.. The figure 5.represents the operation of the RF classifier. The more is the construction of decision trees the higher is the accuracy for the classification. the biggest bulk of call trees is taken into account for any preprocessing.

The figure 6. represents the support vector machine. The points that are nighest to the road are thought-about for the SVM. It consists of a hyperplane within which the positive and negative hyperplane is calculated for additional process of the information. If the separation between the categories is wider, svm tries to form a choice boundary.

ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538 Volume 10 Issue VI July 2022- Available at www. ijraset.com Table | represents the description of the classifiers, datasets and results used from the research work. Based on research work most of them used the SVM and naive bayes classifier, for different types of datasets. In the proposed system, model is implemented using the USA news dataset. Support vector machine, Random forest and LSTM algorithms are used for the USA news dataset which contains 2190 reviews with 2 different class. The accuracy achieved is 85% using support vector machine classifier, 100% using Random forest classifier, 99% using LSTM.

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Sentiment Analysis of Customer Product Reviews using deep Learning and Compare with other Machine Learning Techniques

by amit purohit

2022, International Journal for Research in Applied Science and Engineering Technology

Sentiment analysis is defined as the process of mining of data, view, review or sentence to Predict the emotion of the sentence through natural language processing (NLP) or Machine Learning Techniques. The sentiment analysis involve... more

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Deceptive review detection using labeled and unlabeled data

by Jitendra Rout

2022, Multimedia Tools and Applications

Availability of millions of products and services on e-commerce sites makes it difficult to search the best suitable product according to the requirements because of existence of many alternatives. To get rid of this the most popular and... more

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Fake Review Detection Using Behavioral and Contextual Features

by jay kumar

2022, ArXiv

User reviews reflect significant value of product in the world of e-market. Many firms or product providers hire spammers for misleading new customers by posting spam reviews. There are three types of fake reviews, untruthful reviews,... more

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Research Paper on Fake Online Reviews Detection using Semi-supervised and Supervised learning

by IJRASET Publication

2022, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

Today's business and commerce are heavily influenced by online reviews. Most online product purchase decisions are based on customer reviews. As a result, opportunistic individuals or groups seek to shake product reviews in their favor.... more

© IJRASET: All Rights are Reserved | SJImpact Factor 7.538 | ISRA Journal Impact Factor 7.894 |

It is the main building block of object-oriented modelling. It is used for general conceptual modelling of the structure of the application, and for detailed modeling, translating the models into programming code.

It is a type of interaction diagram because it describes how and what in order. It is used to understand requirements for a new system or to document an existing process.

Detection of online fake reviews helps to detect and filter fake reviews in online websites and e-commerce platform. This project has been implemented based on Python programming language on DJANGO framework with SQL database in the backend. This system filters all authentic reviews with any fake reviews in any online platform such as websites and e-commerce sites. Any user can register with the system and make use of the reviews and review id to detect if it is an authentic review or not. This system will generate the results based on the calculation from the machine learning algorithm used with it.

After successful login, a user is directed to this page. A user can enter a review or a review id to check if it is a fake review or not.

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Survey on Comprehensive Study of Fake Reviews and Reviewers Detection using machine learning techniques

by Mayuri lahire

2022, International Journal for Research in Applied Science and Engineering Technology

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Spam Detection by Using KNN Algorithm Techniques

by Vrushali Dhumal

2022

Abstract: Preceding purchasing an item, individuals typically advise themselves by perusing on the web reviews. To make more benefit dealers frequently attempt to fake client experience. As clients are being misdirected along these lines,... more

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Survey on Comprehensive Study of Fake Reviews and Reviewers Detection using machine learning techniques

by IJRASET Publication

2022, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

Individuals and businesses are increasingly using opinionated social media, such as product evaluations, to make decisions. People, however, try to game the system for profit or fame by opinion spamming (e.g., creating bogus reviews) to... more

$A review where the product was bought by the reviewer holds more credibility than the opposing case. This feature determines the fraction of reviews posted by the group for the brand. This analyzes the review text that what kind of views are there is it positive, negative or neutral, for the given group and brand pair and finds out the average sentiment of these reviews. H. Average Sentiment$

Depending upon the literature proposed by Bing Liu al. [17], there are generally three types of fake/spam reviews as identified: However, recognizing fake reviewers who are writing fake reviews is more important in the effort to detect review spam. So, let’s discuss in detail the type of spammers/fake reviewers. There can be two types as mentioned below:

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Survey in Online social media Skelton by network based spam

by vishal ghogare

2022

These days, a major piece of individuals depend on accessible substance in web-based social networking in their choices (e.g. audits and criticism on a subject or item). The likelihood that anyone can clear out a survey give a brilliant... more

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Combining Clustering with Classification for Spam Detection in Social Bookmarking Systems ?

by Antonia Kyriakopoulou

2022

This paper addresses the problem of learning to classify texts by exploiting information derived from both training and testing sets. To accomplish this, clustering is used as a complementary step to text classification, and is applied... more

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A Novel Feature Set for Application Identification

by Hussein Oudah

2022, International Journal for Information Security Research

Classifying Internet traffic into applications is vital to many areas, from quality of service (QoS) provisioning, to network management and security. The task is challenging as network applications are rather dynamic in nature, tend to... more

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Classification Aided by Clustering : a Literature Review

by Antonia Kyriakopoulou

2021

Supervised and unsupervised learning have been the focus of critical research in the areas of machine learning and artificial intelligence. In the literature, these two streams flow independently of each other, despite their close... more

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Approach to Hypertext Categorization

by Max Bramer

2021

Hypertext/text domains are characterized by several tens or hundreds of thousands of features. This represents a challenge for supervised learning algorithms which have to learn accurate classifiers using a small set of available training... more

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Semi-supervised

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