Detecting Fake Reviews of Hype About Restaurants by Sentiment Analysis

2014 ◽  
pp. 22-30 ◽  
Author(s):  
Run Yu Chen ◽  
Jin Yi Guo ◽  
Xiao Long Deng
Keyword(s):  
Sentiment Analysis ◽  
Fake Reviews

scholarly journals Identification of Fraud Apps Using Sentiment Analysis Techniques

2021 ◽  
Vol 15 (23) ◽  
pp. 178-185
Author(s):  
Abeer Aljumah ◽  
Amjad Altuwijri ◽  
Thekra Alsuhaibani ◽  
Afef Selmi ◽  
Nada Alruhaily
Keyword(s):  
Sentiment Analysis ◽  
User Feedback ◽  
Sensitive Data ◽  
User Reviews ◽  
Analysis Techniques ◽  
Android App ◽  
Fake Reviews ◽  
Google Play

Considering that application’s security is an important aspect, especially nowadays with the increase in technology and the number of fraudsters. It should be noted that determining the security of an application is a difficult task, especially since most fraudsters have become skilled and professional at manipulating people and stealing their sensitive data. Therefore, we pay attention to spot insecure apps by analyzing user feedback on Google Play platform using sentiment analysis. As it is known, user reviews reflect their experiments and experiences in addition to their feelings and satisfaction with the application. But unfortunately, not all of these reviews are real, fake reviews do not reflect the sincerity of feelings, so we have been keen in our work to filter the reviews and deliver accurate and correct results. This tool is useful for both users wanting to install an android app and for developers interested in app’s optimization.

Detection of fake reviews using NLP &Sentiment Analysis

2021 ◽  
Author(s):  
P. Devika ◽  
A. Veena ◽  
E. Srilakshmi ◽  
A. Ranavardhan Reddy ◽  
E. Praveen
Keyword(s):  
Sentiment Analysis ◽  
Fake Reviews

scholarly journals Android Apps Security Assessment using Sentiment Analysis Techniques: Comparative Study

2021 ◽  
Vol 15 (24) ◽  
pp. 123-133
Author(s):  
Abeer Aljumah ◽  
Amjad Altuwijri ◽  
Thekra Alsuhaibani ◽  
Afef Selmi ◽  
Nada Alruhaily
Keyword(s):  
Comparative Study ◽  
Sentiment Analysis ◽  
Security Assessment ◽  
Sensitive Data ◽  
User Reviews ◽  
Application Security ◽  
Analysis Techniques ◽  
Android Apps ◽  
Fake Reviews ◽  
Google Play

Considering that application security is an important aspect, especially nowadays with the increase in technology and the number of fraudsters. It should be noted that determining the security of an application is a difficult task, especially since most fraudsters have become skilled and professional at manipulating people and stealing their sensitive data. Therefore, we pay attention to trying to spot insecurity apps, by analyzing user feedback on the Google Play platform and using sentiment analysis to determine the apps level of security. As it is known, user reviews reflect their experiments and experiences in addition to their feelings and satisfaction with the application or not. But unfortunately, not all of these reviews are real, and as is known, the fake reviews do not reflect the sincerity of feelings, so we have been keen in our work to filter the reviews to be the result is accurate and correct. This study is useful for both users wanting to install android apps and for developers interested in app optimization.

scholarly journals Product Recommendation System based on User Trustworthiness & Sentiment Analysis

2020 ◽  
Vol 32 ◽  
pp. 03030
Author(s):  
Gunjeet Kaur Soor ◽  
Amey Morje ◽  
Rohit Dalal ◽  
Deepali Vora
Keyword(s):  
Sentiment Analysis ◽  
Recommendation System ◽  
Opinion Mining ◽  
Product Recommendation ◽  
Fake Reviews ◽  
New System

The current online product recommendation system based on reviews has many limitations due to randomness in the review patterns. The data which is used are the reviews and ratings from the e-commerce websites. This data might contain fake reviews that make the data uncertain. Due to this, the currently existing systems produce ambiguous results on this present data. Instead of this, the new system uses only genuine reviews, considering the trustworthiness of the user and generates the results in a more significant manner. The proposed system scrapes reviews from different online websites and performs opinion mining and sentiment analysis on it. Other factors like star ratings, the buyer’s profile and previous purchases and whether the review has been given after purchasing or not are included. Based on these factors & user trustworthiness, the website from which the user should buy the product will be recommended.

scholarly journals Web Objects Opinion through Sentiment Engineering

2019 ◽  
Vol 9 (2S) ◽  
pp. 473-476
Keyword(s):  
Comparative Study ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Performance Metrics ◽  
User Generated Content ◽  
Web Objects ◽  
Fake Reviews ◽  
Open Issues

Sentiment Analysis is the analysis of thoughts, feelings and qualities of people towards an object. Automatically recognizing user-generated content views is of great help for commercial and political use. Sentiment Analysis / Opinion Mining lets us gather information about the positive and negative characteristics of any given object / product, and we recommend the favorable and highly scoring views on the object / product to the user. Although researchers have contributed a lot towards objects review through sentiment analysis, still there are open issues needs to be addressed such as Negation Handling, Domain Generalization and Detection and Removal of Fake Reviews. This paper presents a review on the various algorithms used for Negation Handling, Domain Generalization and Detection and Removal of Fake Reviews along with a comparative study against performance metrics along with their limitations.

scholarly journals Recommendation of Distance Education Colleges to Students based on Alumni Feedback

2020 ◽  
Vol 9 (1) ◽  
pp. 2357-2363
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Mining System ◽  
Product Service ◽  
Effective System ◽  
Analysis System ◽  
The Individual ◽  
Implicit And Explicit ◽  
Fake Reviews

Sentiment Analysis (SA) systems are very common because most people trust it based on the opinions, emotions, attitudes and feelings shared by the users for decision making purposes about the product, service, news analytics etc. Sentiment analysis or opinion mining is used to automatically detect and classify sentiments into positive, negative or neutral opinion on product or service through certain algorithms. The expeditious growth of internet leads to the increase of reviews about product, services, movies, restaurants or vacation destinations and organizations. In order to increase or decrease the market value of the product, spammers may give the fake ratings. Sentiment Analysis system face great difficulties in deploying the algorithms to classify each review as either honest review, posted by the customers after using the products, or spam review, posted by the individual spammer or spammer groups. Another major challenge faced by the sentiment analysis system is that it lacks the accuracy of predicting implicit and explicit features present in the dataset is low, which is the major challenge in opinion mining system. The proposed system deals with text pre-processing which helps in improving the overall performance of the sentiment analysis systems and an effective system is developed to identify the fake reviews present in the dataset. Association Rule Mining along with K-Means clustering is used to achieve higher efficiency in classification of implicit and explicit features. Lexicon method is used for the classification of sentiments into positive and negative polarities. The advantage of proposed system is that, it can identify and remove the fake reviews in the dataset and extraction of both implicit and explicit feature can be identified through Lexicon based Method along with its polarities.

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

An historical analysis of species references in American English

Corpora ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. 327-349
Author(s):  
Craig Frayne
Keyword(s):  
Environmental Change ◽  
Sentiment Analysis ◽  
Quantitative Methods ◽  
English Language ◽  
Language Use ◽  
American English ◽  
Historical Analysis ◽  
The Past ◽  
Corpus Studies ◽  
Google Books

This study uses the two largest available American English language corpora, Google Books and the Corpus of Historical American English (coha), to investigate relations between ecology and language. The paper introduces ecolinguistics as a promising theme for corpus research. While some previous ecolinguistic research has used corpus approaches, there is a case to be made for quantitative methods that draw on larger datasets. Building on other corpus studies that have made connections between language use and environmental change, this paper investigates whether linguistic references to other species have changed in the past two centuries and, if so, how. The methodology consists of two main parts: an examination of the frequency of common names of species followed by aspect-level sentiment analysis of concordance lines. Results point to both opportunities and challenges associated with applying corpus methods to ecolinguistc research.

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