Role of Emotions in Fine Dining Restaurant Online Reviews: The Applications of Semantic Network Analysis and a Machine Learning Algorithm

2021 ◽  
pp. 1-29
Author(s):  
Munhyang Oh ◽  
Seongseop Kim
Keyword(s):  
Machine Learning ◽  
Network Analysis ◽  
Learning Algorithm ◽  
Semantic Network ◽  
Online Reviews ◽  
Machine Learning Algorithm ◽  
Semantic Network Analysis

Understanding the expression of grievances in the Arabic Twitter-sphere using machine learning

2019 ◽  
Vol 5 (2) ◽  
pp. 108-119
Author(s):  
Yeslam Al-Saggaf ◽  
Amanda Davies
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Network ◽  
Network Analysis ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Content Type ◽  
The Social ◽  
Twitter Users

Purpose The purpose of this paper is to discuss the design, application and findings of a case study in which the application of a machine learning algorithm is utilised to identify the grievances in Twitter in an Arabian context. Design/methodology/approach To understand the characteristics of the Twitter users who expressed the identified grievances, data mining techniques and social network analysis were utilised. The study extracted a total of 23,363 tweets and these were stored as a data set. The machine learning algorithm applied to this data set was followed by utilising a data mining process to explore the characteristics of the Twitter feed users. The network of the users was mapped and the individual level of interactivity and network density were calculated. Findings The machine learning algorithm revealed 12 themes all of which were underpinned by the coalition of Arab countries blockade of Qatar. The data mining analysis revealed that the tweets could be clustered in three clusters, the main cluster included users with a large number of followers and friends but who did not mention other users in their tweets. The social network analysis revealed that whilst a large proportion of users engaged in direct messages with others, the network ties between them were not registered as strong. Practical implications Borum (2011) notes that invoking grievances is the first step in the radicalisation process. It is hoped that by understanding these grievances, the study will shed light on what radical groups could invoke to win the sympathy of aggrieved people. Originality/value In combination, the machine learning algorithm offered insights into the grievances expressed within the tweets in an Arabian context. The data mining and the social network analyses revealed the characteristics of the Twitter users highlighting identifying and managing early intervention of radicalisation.

Smart Campus

2021 ◽  
pp. 161-169
Author(s):  
Sushant Keni ◽  
Priyanka Jadhav ◽  
Mayur Patil ◽  
Prof. Sonal Chaudhari
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
Network Analysis ◽  
Learning Algorithm ◽  
Third Parties ◽  
Machine Learning Algorithm ◽  
Good Service ◽  
Smart Campus ◽  
Recruitment System

We evaluate the feasibility of using Facebook data to enhance the effectiveness of a recruitment system, especially for résumé verification and recognize the personality by using social network analysis methods. In the industries employee’s personality is very important in the workplace which will help to growth of the company and give more good service to the client. Currently resume verification is based on trustful third parties who does background verification. Based on this report is sent to the company who is hiring the employee decides to keep employee or not. This manual system usually takes lots of time and this system generally wont display candidates’ nature towards society (in short how he behaves in society weather he posts something wrong on social media in simple words his/her personality). Social media now a days is huge platform where user generally spends too much time on social media like Facebook, LinkedIn etc. like posting a page, commenting, liking the post, certification uploading, adding friends. We are going to design such a system that verifies genuineness of user by scraping or exploring data from Facebook or LinkedIn or both. we are exploring post of person and classifies it into is it technology related, violence related and many more what are the comments he gives on his post how he reacts his language of handling a query will be parsed and classified using machine learning algorithm of previously trained dataset using SVM. And at the end we will show this information to the company to make their own decision based on this result.

Spam and Fake Spam Message Detection Framework Using Machine Learning Algorithm

2020 ◽  
Vol 17 (8) ◽  
pp. 3444-3448
Author(s):  
S. L. Jany Shabu ◽  
V. Netaji Subhash Chandra Bose ◽  
Venkatesh Bandaru ◽  
Sardar Maran ◽  
J. Refonaa
Keyword(s):  
Machine Learning ◽  
Social Networking ◽  
Learning Algorithm ◽  
Detection System ◽  
Life Stage ◽  
Online Reviews ◽  
The Internet ◽  
Machine Learning Algorithm ◽  
Web Based ◽  
Web Logs

Online reviews about the acquisition of items or administrations gave have become the primary wellspring of clients’ conclusions. So as to pick up benefit or acclaim, as a rule spam reviews are composed to advance or downgrade a couple of target items or administrations. This training is known as review spamming. In the previous barely any years, an assortment of strategies have been proposed so as to illuminate the issue of spam reviews. It is a mainstream correspondence and furthermore known as information trade media. Information could be of a book, numbers, figures or insights that are gotten to by a PC. These days, numerous individuals relies upon substance accessible in web-based social networking in their choices. Sharing of data with people groups has additionally pulled in social spammers to endeavor and spread spam messages to advance individual web logs, notices, advancements, phishing, trick, fakes, etc. The possibility that anyone will leave a review give a brilliant possibility for spammers to post spit audit with respect to item and administrations for different interests and possibilities. In this way, we propose a fake message detection system utilizing ML to recognize the spam and fake messages on the internet based life stage.

scholarly journals Acute Diseases Prognosis using Chatbot

2021 ◽  
Vol 9 (VII) ◽  
Author(s):  
Shivanand Tiwari
Keyword(s):  
Machine Learning ◽  
Healthcare Professionals ◽  
Learning Algorithm ◽  
Early Stage ◽  
Machine Learning Algorithm ◽  
Patient Needs ◽  
Healthcare Organizations ◽  
Physician Time ◽  
Consultant Doctor

The role of chatbots in healthcare is to help free-up valuable physician-time by reducing or eliminating unnecessary doctor’s appointments. As the increase in cost, various healthcare organizations are looking for different ways to manage cost while improving the user’s experience. As we know there is shortage of healthcare professionals that makes it increasingly necessary for us to augment technology with health facilities in order to allow doctors to focus on more critical patient needs. Keeping this in Mind we are aiming to develop a Project that will basically ask for Symptoms from the Patient and perform the Prognosis on the basis of already developed dataset. The Machine Learning Algorithm will work on that dataset of symptoms and their prognosis to tell exactly what has happened to the Patient and will help to Reach the Desired Consultant/Doctor with respect to the Prognosis. It will also help the Patients to get Useful Information regarding different diseases that may help to deal with some Chronic Diseases at an early Stage!’

scholarly journals Aislamiento social obligatorio: un análisis de sentimientos mediante machine learning

2021 ◽  
Vol 12 (26) ◽  
pp. 1-13
Author(s):  
Carlos Alberto Arango Pastrana ◽  
Carlos Fernando Osorio Andrade
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Learning Algorithm ◽  
Data Sets ◽  
Machine Learning Algorithm ◽  
Economic Problems ◽  
Colombian Government

To reduce the rate of contagion by Covid-19, the Colombian government has adopted, among other measures, for mandatory isolation, with divided opinions, because despite helping to reduce the spread of the virus, it generates mental and economic problems that are difficult to overcome. The objective of this document was to analyze the underlying sentiments in the Twitter comments related to isolation, identifying the topics and words most frequently used in this context. A machine learning algorithm was built to identify sentiments in 72,564 posts and a social network analysis was applied establishing the most frequent topics in the data sets. The results suggest that the algorithm is highly accurate in classifying feelings. Also, as the isolation extends, comments related to the quarantine grow proportionally. Fear was identified as the predominant feeling throughout the period of confinement in Colombia.

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