WITHDRAWN: Evaluating business Yelp’s star ratings using sentiment analysis

Ajrawi, Shams Al; Agrawal, Akash; Mangal, Harshita; Putluri, Kavya; Reid, B.K.; Hanna, Ghassan Faraj; Sarkar, Mitali

doi:10.1016/j.matpr.2020.12.137

“…The twitter data has labels as 4 types of sentiments {happiness, relief, enthusiasm, surprise} the data size: 8040 tweets. The AmazonCellPhone.csv file has class label as 5 different user rating [1,2,3,4,5] the data size is 20000 reviews. After importing the data, the step applied for tokenization of the text document.…”

Section: Methodsmentioning

confidence: 99%

“…It is also useful to public in survey of goods and service available in market. Opinion extraction used to perceive the market trends in efficient manner that is used in updating the quality [3,4]. The service providers, manufacturers, distributer and consumers views undergo through impact of response extracted from opinion of users.…”

Section: Introductionmentioning

confidence: 99%

Opinion Extraction using Hybrid Learning Algorithm with Feature Set Optimization Approach

Devendra Kumar

2024

jes

0

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Evolution in engineering and technology added large size of data storage and transmission through the web application over the internet. This huge amount of data primarily used for exchange of information in between users and devices and in secondary aspects it has utilization as feedback, ratings and reviews that is supporting in generation of useful information of products, services, incidents etc. The data as opinion, feedback, view & suggestion is explored, organized & analyzed for selection of appropriate options. Sentiment analysis using the opinion extraction is a challenging task that is based on feature extraction and the concepts of Natural Language Processing that is applied in identification of the opinions of a user in terms of positive, neutral or negative ratings hidden in the form of comments typed as the text. Presently many data-processing based feature evaluation techniques for opinion extraction are used for solving the issues faced under sentiment classification applications. This article is based on development and application of algorithms for opinion extraction from text data available on web resources by K-Nearest Neighbor (KNN), Support vector machine (SVM) and hybrid of both named as SVM+KNN for classification of multi-label opinions from extracted text from review data of Twitter and Amazon. The performance of all the classification models (KNN, SVM and SVM+KNN) on both datasets is evaluated in terms of different parameters.

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“…In [39], Shahriar et al proposed a framework for the detection of Android permissions that are anomalous. The detection system focused on the identification of relevant categories by applying a latent semantic index [40,41]. The result of the study shows that dangerous permissions constitute a security vulnerability.…”

Section: Permission-based Detection and Feature Extractionmentioning

confidence: 99%

Modeling Correlation between Android Permissions Based on Threat and Protection Level Using Exploratory Factor Plane Analysis

Ashawa

¹

,

Morris

²

2021

JCP

3

0

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The evolution of mobile technology has increased correspondingly with the number of attacks on mobile devices. Malware attack on mobile devices is one of the top security challenges the mobile community faces daily. While malware classification and detection tools are being developed to fight malware infection, hackers keep deploying different infection strategies, including permissions usage. Among mobile platforms, Android is the most targeted by malware because of its open OS and popularity. Permissions is one of the major security techniques used by Android and other mobile platforms to control device resources and enhance access control. In this study, we used the t-Distribution stochastic neighbor embedding (t-SNE) and Self-Organizing Map techniques to produce a visualization method using exploratory factor plane analysis to visualize permissions correlation in Android applications. Two categories of datasets were used for this study: the benign and malicious datasets. Dataset was obtained from Contagio, VirusShare, VirusTotal, and Androzoo repositories. A total of 12,267 malicious and 10,837 benign applications with different categories were used. We demonstrate that our method can identify the correlation between permissions and classify Android applications based on their protection and threat level. Our results show that every permission has a threat level. This signifies those permissions with the same protection level have the same threat level.

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“…It is also useful to public in survey of goods and service available in market. Opinion extraction used to perceive the market trends in efficient manner that is used in updating the quality [3,4]. The service providers, manufacturers, distributer and consumers views undergo through impact of response extracted from opinion of users.…”

Section: Introductionmentioning

confidence: 99%

Opinion Extraction using Hybrid Learning Algorithm with Feature Set Optimization Approach

Devendra Kumar

2024

jes

0

View full text Add to dashboard Cite

Evolution in engineering and technology added large size of data storage and transmission through the web application over the internet. This huge amount of data primarily used for exchange of information in between users and devices and in secondary aspects it has utilization as feedback, ratings and reviews that is supporting in generation of useful information of products, services, incidents etc. The data as opinion, feedback, view & suggestion is explored, organized & analyzed for selection of appropriate options. Sentiment analysis using the opinion extraction is a challenging task that is based on feature extraction and the concepts of Natural Language Processing that is applied in identification of the opinions of a user in terms of positive, neutral or negative ratings hidden in the form of comments typed as the text. Presently many data-processing based feature evaluation techniques for opinion extraction are used for solving the issues faced under sentiment classification applications. This article is based on development and application of algorithms for opinion extraction from text data available on web resources by K-Nearest Neighbor (KNN), Support vector machine (SVM) and hybrid of both named as SVM+KNN for classification of multi-label opinions from extracted text from review data of Twitter and Amazon. The performance of all the classification models (KNN, SVM and SVM+KNN) on both datasets is evaluated in terms of different parameters.

show abstract

WITHDRAWN: Evaluating business Yelp’s star ratings using sentiment analysis

Cited by 10 publications

References 6 publications

Opinion Extraction using Hybrid Learning Algorithm with Feature Set Optimization Approach

Opinion Extraction using Hybrid Learning Algorithm with Feature Set Optimization Approach

Modeling Correlation between Android Permissions Based on Threat and Protection Level Using Exploratory Factor Plane Analysis

Opinion Extraction using Hybrid Learning Algorithm with Feature Set Optimization Approach

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