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

2020 ◽  
Vol 17 (8) ◽  
pp. 3577-3580
Author(s):  
M. S. Roobini ◽  
B. Nikhil Chowdary ◽  
J. Madhav Chowdary ◽  
J. Aruna ◽  
Anitha Ponraj
Keyword(s):  
Text Mining ◽  
Supervised Learning ◽  
Online Shopping ◽  
Online Reviews ◽  
Business Organizations ◽  
Main Aspect ◽  
Web Based ◽  
Control Item

Online reviews have an incredible effect on the present business and trade. The development of web-based business organizations has pulled in numerous buyers since they provide a scope of items on aggressive costs. The main aspect most buyers depends on while doing online shopping is the review of items for closing the choice of object. Basic leadership for the acquisition of online items generally relies upon reviews given by the clients. Henceforth, deft people or gatherings attempt to control item surveys for their advantages. In perspective on the impacts of these phony surveys, various systems to recognize these were proposed in the research. Because of reviews and its nature, this is hard to group these utilizing only one classifier. Henceforth, the present research discusses a classifier for dealing with identifying such phony reviews. The study also presents the text mining techniques both supervised and semi-supervised to identify counterfeit online reviews just as looks at the effectiveness of the two strategies on the datasets with hotel surveys.

scholarly journals Automated Evaluation of Students’ Feedbacks using Text Mining Methods

2019 ◽  
Vol 8 (4) ◽  
pp. 337-342
Keyword(s):  
Text Mining ◽  
Online Reviews ◽  
Structured Data ◽  
Unstructured Data ◽  
Future Perspectives ◽  
Web Based ◽  
Literary Structure ◽  
Automated Evaluation ◽  
Teaching Learning ◽  
Textual Form

In this era of competition there is a culture of online reviews or feedbacks. These feedbacks may be about any product or service. However, major issues are their unstructured textual form and big number. It means every user gives feedback in own style. Study and analyzing of such unorganized big number of feedbacks that are growing every year becomes herculean task. This paper describes about mining of structured data (table) and unstructured data (text) both. An application from academic environment for structured and unstructured form of data is considered and discussed to enhance understanding and easiness of researcher. Stanford Parser plays a very useful role to understand the semantic of a sentence. It gives a base that how to separate data from the wellspring of information accessible in the literary structure like web based life, tweets, news, books and so on. It is also helpful to judge a teaching learning process in terms of teacher’s performance and subject’s weakness if any. This paper has five sections first about introduction, second about literature of text mining and its techniques, third about proposed work and result, fourth about future perspectives and finally fifth as a conclusion.

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

2020 ◽  
pp. 428-431
Author(s):  
Aneel Narayanapur ◽  
Pavankumar Naik ◽  
Suraksha G ◽  
Pavitra S I ◽  
Shruddha Mudigoudar ◽  
...  
Keyword(s):  
Decision Making ◽  
Text Mining ◽  
Supervised Learning ◽  
Online Reviews ◽  
Product Reviews ◽  
Data Set ◽  
Or Groups

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 reviews for their own interests. This paper introduces some semi-supervised and supervised text mining models to detect fake online reviews as well as compares the efficiency of both techniques on data set containing hotel reviews.

Factor Influencing Consumers on Beguiling Craze of Online Shopping: A Study of Attitude in Nagpur City of Maharashtra (India)

2019 ◽  
Vol 118 (11) ◽  
pp. 137-145
Author(s):  
Sohail Imran Khan ◽  
Rohat Zada
Keyword(s):  
Online Shopping ◽  
Young Generation ◽  
Web Based ◽  
High Significance ◽  
Online Fraud ◽  
Return Policy ◽  
Large State ◽  
Evolution Of Technology ◽  
Center City ◽  
General Product

Evolution of technology has completely revolutionaries’ day to day life of common man. Technology has penetrated in our life like anything. These days everybody is using technology for their benefit’s and marketers are no an exception to it. They are using technology to reach to the customers. Days are far gone when people used to line up in stores to purchase the general product. These days, more and more individuals lean toward online shopping, which is presently a pattern of style and fashion. Nagpur, the center city of the country and world-famous for its oranges is advancing towards computerized explosion that makes high significance on the assessment of the present acknowledgment level of online shopping by the youngsters. In this way, understanding the by and a large state of customer's attitude towards web-based shopping is significant for the Nagpurians. In this study, 143 respondents took part in the survey. Respondents were selected through simple random technique. Data was analyzed using SPSS Version 22. This study found that online shopping is very common in this young generation of Nagpur. Major reason for Nagpurains to do online shopping is that it saves a lot of time. However, consumer those who do not shop online is only because of online fraud, lack of personal touch and no return policy. Nagpur consumers do prescribe online shopping as an elective path for shopping.

scholarly journals Research on comment target extracting in Chinese online shopping platform

2018 ◽  
Vol 2 (3) ◽  
pp. 247-258
Author(s):  
Zhishuo Liu ◽  
Qianhui Shen ◽  
Jingmiao Ma ◽  
Ziqi Dong
Keyword(s):  
Association Rule ◽  
Online Shopping ◽  
Low Frequency ◽  
Online Reviews ◽  
Content Type ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Frequency Feature ◽  
Practical Implications ◽  
Data Reading

Purpose This paper aims to extract the comment targets in Chinese online shopping platform. Design/methodology/approach The authors first collect the comment texts, word segmentation, part-of-speech (POS) tagging and extracted feature words twice. Then they cluster the evaluation sentence and find the association rules between the evaluation words and the evaluation object. At the same time, they establish the association rule table. Finally, the authors can mine the evaluation object of comment sentence according to the evaluation word and the association rule table. At last, they obtain comment data from Taobao and demonstrate that the method proposed in this paper is effective by experiment. Findings The extracting comment target method the authors proposed in this paper is effective. Research limitations/implications First, the study object of extracting implicit features is review clauses, and not considering the context information, which may affect the accuracy of the feature excavation to a certain degree. Second, when extracting feature words, the low-frequency feature words are not considered, but some low-frequency feature words also contain effective information. Practical implications Because of the mass online reviews data, reading every comment one by one is impossible. Therefore, it is important that research on handling product comments and present useful or interest comments for clients. Originality/value The extracting comment target method the authors proposed in this paper is effective.

scholarly journals How the Covid-19 Pandemic Is Changing Online Food Shopping Human Behaviour in Italy

2020 ◽  
Vol 12 (22) ◽  
pp. 9594
Author(s):  
Leonardo Salvatore Alaimo ◽  
Mariantonietta Fiore ◽  
Antonino Galati
Keyword(s):  
Online Shopping ◽  
Grocery Shopping ◽  
Competitive Advantages ◽  
Web Based ◽  
Explanatory Variables ◽  
Food Shopping ◽  
Cumulative Logit Model ◽  
Level Of Satisfaction ◽  
Shopping Experience ◽  
On Line

The advent of the Internet has significantly changed consumption patterns and habits. Online grocery shopping is a way of purchasing food products using a web-based shopping service. The current COVID-19 pandemic is determining a rethinking of purchase choice elements and of consumers’ behavior. This work aims to investigate which characteristics can affect the decision of online food shopping during the pandemic emergency in Italy. In particular, the work aims to analyze the effects of a set of explanatory variables on the level of satisfaction for the food online shopping experience. For achieving this aim, the proportional odds version of the cumulative logit model is carried out. Data derive from an anonymous on-line questionnaire administrated during the first months of the pandemic and filled by 248 respondents. The results of this work highlight that people having familiarity with buying food online, that have a higher educational level and consider food online channels easy to use, appear more satisfied for the food online shopping experience. These findings can be crucial for the future green global challenges as online shopping may help to reach competitive advantages for company sustainability.

scholarly journals GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Chih-Hsuan Wei ◽  
Hung-Yu Kao ◽  
Zhiyong Lu
Keyword(s):  
Text Mining ◽  
Protein Domains ◽  
Gene Families ◽  
Integrative Approach ◽  
Name Disambiguation ◽  
Gene Mention ◽  
Web Based ◽  
Gene Normalization ◽  
Document Level ◽  
Gene Normalization Task

The automatic recognition of gene names and their associated database identifiers from biomedical text has been widely studied in recent years, as these tasks play an important role in many downstream text-mining applications. Despite significant previous research, only a small number of tools are publicly available and these tools are typically restricted to detecting only mention level gene names or only document level gene identifiers. In this work, we report GNormPlus: an end-to-end and open source system that handles both gene mention and identifier detection. We created a new corpus of 694 PubMed articles to support our development of GNormPlus, containing manual annotations for not only gene names and their identifiers, but also closely related concepts useful for gene name disambiguation, such as gene families and protein domains. GNormPlus integrates several advanced text-mining techniques, including SimConcept for resolving composite gene names. As a result, GNormPlus compares favorably to other state-of-the-art methods when evaluated on two widely used public benchmarking datasets, achieving 86.7% F1-score on the BioCreative II Gene Normalization task dataset and 50.1% F1-score on the BioCreative III Gene Normalization task dataset. The GNormPlus source code and its annotated corpus are freely available, and the results of applying GNormPlus to the entire PubMed are freely accessible through our web-based tool PubTator.

scholarly journals Explaining and Predicting Helpfulness & Funniness of Online Reviews on the Steam Platform

2021 ◽  
Vol 29 (6) ◽  
pp. 0-0
Keyword(s):  
Applied Research ◽  
Online Shopping ◽  
Product Information ◽  
Online Reviews ◽  
Online Review ◽  
Gradient Boosting ◽  
Related Data ◽  
Review Helpfulness ◽  
Display Content ◽  
The Impact

Online review is a crucial display content of many online shopping platforms and an essential source of product information for consumers. Low-quality reviews often cause inconvenience to the platform and review readers. This article aims to help Steam, one of the largest digital distribution platforms, predict the review helpfulness and funniness. Via Python, 480,000 game reviews related data for 20 games were captured for analysis. This article analyzed the impact of three categories of influencing factors on the usefulness and funniness of game reviews, which are characteristics of review, reviewer and game. Additionally, by using the Random Forest-based classifier, the usefulness of reviews could be accurately predicted, while for funniness, Gradient Boosting Decision Tree was the better choice. This article applied research on the usefulness of reviews to game products and proposed research on the funniness of reviews.

scholarly journals Customer Perception Towards Online Shopping in Chennai City

2019 ◽  
Vol 8 (4S3) ◽  
pp. 278-280
Keyword(s):  
Online Shopping ◽  
The Internet ◽  
Internet Shopping ◽  
Web Based ◽  
Effective Utilization ◽  
Customer Perception ◽  
The World ◽  
Online Stores ◽  
Chennai City ◽  
Number Of Individuals

Online based purchasing is the way toward buying products and enterprises from traders who sell them online through Internet. Since the rise of the World Wide Web, sellers have tried to offer their items to individuals who browser the Internet. Customers can visit online stores from their homes and shop comfortably. Presently a day shopping has turned out to be mainstream among individuals through browsing which has increased their web knowledge and effective utilization of internet. So internet shopping has become accustomed to the buyers which made the researcher to study the perception on internet based shopping. The principle aim of the this research is to find out the opinion of the respondents towards internet shopping. These days, there has been a flood in web based shopping. The Internet has been utilized by clothing organizations to sell their items and advance their brands. As an ever increasing number of individuals purchase attire on the web, there have been an expanding number of inquires about.

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