scholarly journals An Approach for Movie Review Classification in Turkish

2021 ◽  
Vol 4 (2) ◽  
pp. 56
Author(s):  
Migena Ceyhan ◽  
Zeynep Orhan ◽  
Dimitrios Karras
Keyword(s):  
Social Media ◽  
Text Analysis ◽  
Machine Learning Algorithms ◽  
Pure Gold ◽  
Huge Number ◽  
Linear Approach ◽  
The Right ◽  
Critical Words ◽  
Negative Sense ◽  
Significant Class

Web 2.0 has given to all people the right to become a representative of a huge cast of informal media. The importance of this power is getting more evident everyday. Every social media actor can influence the rest of the world by one’s own opinions, feelings, and thoughts generously shared on multiple media. This information belonging to various fields of life can be very handy and be used to one’s advantage, gaining precious experience. One of the greatest problems that this poses is the huge number of data spread everywhere, which are difficult to process as row data per se. Social media and general sentiment text analysis is of much valuable use, accomplishing the task extracting pure gold out of raw mineral. The key point of this investigation is to characterize new reviews automatically. To start with, features selected out of all the word roots appearing in the comments were used to train the system according to known machine learning algorithms. Next, critical words determining positive or negative sense were extracted. Another strategy was attempted eliminating common terms and dealing only with the significant class-determining words to build vocabulary with them. Aparts from linear approach, vector based feature sets were prepared out all or some of the features. The outcomes acquired were analyzed and compared leading to important conclusions, emphasizing the importance of feature selection in text classification.

Wearable IoT intelligent recommender framework for a smarter healthcare approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mona Bokharaei Nia ◽  
Mohammadali Afshar Kazemi ◽  
Changiz Valmohammadi ◽  
Ghanbar Abbaspour
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Wearable Devices ◽  
Machine Learning Algorithms ◽  
Content Type ◽  
Iot Devices ◽  
The Right ◽  
Media Sentiment

PurposeThe increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.Design/methodology/approachThis data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.FindingsThe proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.Research limitations/implicationsThe research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.Practical implicationsThe emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.Originality/valueIn this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

The Soundtrack of the Extreme: Nasheeds and Right-Wing Extremist Music as a “Gateway Drug” into the Radical Scene?

2020 ◽  
Vol 58 (2) ◽  
pp. 291-334
Author(s):  
Veronika Möller ◽  
Antonia Mischler
Keyword(s):  
Social Media ◽  
Text Analysis ◽  
Right Wing ◽  
New Members ◽  
Perceived Need ◽  
Analysis Methods ◽  
Violent Content ◽  
Everyday Experiences ◽  
Extremist Groups ◽  
The Right

AbstractMusic plays an important role in both the right-wing extremist and the Salafi jihadist scenes as a unifying and radicalizing factor. It is used to share propaganda and highlight specific ideologies. Music is disseminated by various means, e.g. via social media, and used strategically to attract potential new members. The aims of right-wing extremist music and the Salafi jihadist nasheeds are, among other things, to inspire the youth, reach out to a worldwide audience of potential sympathizers, and disseminate their absolutist worldview. To achieve these goals, seemingly objective depictions of negative everyday experiences, of oppression, and the need for resistance are utilized. The songs are usually associated with violent content, and in conjunction with videos, they illustrate the perceived need to defend oneself. In this article, we will take a closer look at the content of four selected extremist songs. Our analysis of the content is based on a triangulation of sequential text analysis methods and identifies the differences and comparable elements of the ideologies in a final step. In addition to the content, the research aims to examine the possible effects of extremist groups’ music.

scholarly journals Fake News Detection on Social Media using Machine Learning Techniques

2020 ◽  
Vol 9 (7) ◽  
pp. 940-943
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Huge Number ◽  
Web Based ◽  
Distributed Information ◽  
Learning Techniques ◽  
Day By Day

: Web based life administrations, as Facebook and Twitter, Renren, Instagram, and linkedin have recently become an enormous and persistent supply of day by day news. These stages give a huge number of clients and give numerous administrations, for example, content arrangement and distributing. Not all distributed information via internet based medium is dependable and exact. Numerous individuals attempt to distribute fake and mistaken news so as to control general conclusion. Counterfeit news might be intentionally made to advance money related, political and public premiums, and can lead to unsafe effects on people convictions and choices.. In this paper we examine different systems for recognizing counterfeit information via internet based networking medium. Our point is to locate a dependable and right model that arranges a given article as fake or genuine. For identification of fake articles we use machine learning algorithms.

Influence of social media marketing towards purchase of residential property

2019 ◽  
Vol 118 (6) ◽  
pp. 145-149
Author(s):  
A. Ekanthalingam ◽  
Dr. A. Gopinath
Keyword(s):  
Social Media ◽  
High Speed ◽  
Word Of Mouth ◽  
Marketing Strategies ◽  
Social Media Marketing ◽  
Purchase Decision ◽  
Residential Property ◽  
Customer Delight ◽  
The Right ◽  
Potential Customers

‘Marketing’ is not just an activity. It is a process, a philosophy and a phenomenon. The evolution of marketing has produced tremendous benefits to business and end consumers. The innovation in this field has been steady and yet at high speed. From ‘word of mouth advertising’ which was the only option earlier we are now at the mercy of what consumers are sharing about their experience on the internet. Social Media has become more powerful than what we think and this article shows how we can leverage this to benefit the top-line and customer delight. We dive deep to understand the influence Social Media can create towards purchase of residential property. As much complex it is to make the purchase decision of a property, it is equally difficult for marketers to send the right message to their target audience. Through this article, we are trying to see how marketers have transformed their traditional marketing strategies to address the needs of the millennial population, who are the most potential customers for property purchase.

Techniques of Viral Marketing

2019 ◽  
Vol 118 (6) ◽  
pp. 97-99
Author(s):  
Arockia Jeyasheela A ◽  
Dr.S. Chandramohan
Keyword(s):  
Social Media ◽  
Real Time ◽  
Word Of Mouth ◽  
Viral Marketing ◽  
Consumer Perception ◽  
Key Word ◽  
The Right ◽  
A Company

This study is discussed about the viral marketing. It is a one of the key success of marketing. This paper gave the techniques of viral marketing. It can be delivered word of mouth. It can be created by both the representatives of a company and consumer (individuals or communities). The right viral message with go to right consumer to the right time. Viral marketing is easy to attract the consumer. It is most important advertising to consumer. It involves consumer perception, organization contribution, blogs, SMO (Social Media Optimize), SEO (Social Engine Optimize). Principles of viral marketing are social profile gathering, Proximity Market, Real time Key word density.

Chinese Social Media Platforms and Microblogging in the Context of Public Diplomacy

Communicology ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 167-179
Author(s):  
E.S. Nadezhkina
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Public Diplomacy ◽  
User Generated Content ◽  
The Internet ◽  
Interactive Communication ◽  
Network Interaction ◽  
Social Media Platforms ◽  
The Right ◽  
Chinese Social Media

The term “digital public diplomacy” that appeared in the 21st century owes much to the emergence and development of the concept of Web 2.0 (interactive communication on the Internet). The principle of network interaction, in which the system becomes better with an increase in the number of users and the creation of user-generated content, made it possible to create social media platforms where news and entertainment content is created and moderated by the user. Such platforms have become an expression of the opinions of various groups of people in many countries of the world, including China. The Chinese segment of the Internet is “closed”, and many popular Western services are blocked in it. Studying the structure of Chinese social media platforms and microblogging, as well as analyzing targeted content is necessary to understand China’s public opinion, choose the right message channels and receive feedback for promoting the country’s public diplomacy. This paper reveals the main Chinese social media platforms and microblogging and provides the assessment of their popularity, as well as possibility of analyzing China’s public opinion based on “listening” to social media platforms and microblogging.

RepPer: Perception of Psychiatric Disorders on Twitter in French (Preprint)

2020 ◽  
Author(s):  
Sarah Delanys ◽  
Farah Benamara ◽  
Véronique Moriceau ◽  
François Olivier ◽  
Josiane Mothe
Keyword(s):  
Social Media ◽  
Psychiatric Disorders ◽  
Digital Technology ◽  
Psychotic Disorders ◽  
Negative Polarity ◽  
Machine Learning Algorithms ◽  
Annotation Scheme ◽  
Word Use ◽  
Wide Range ◽  
Initial Dataset

BACKGROUND With the advent of digital technology and specifically user generated contents in social media, new ways emerged for studying possible stigma of people in relation with mental health. Several pieces of work studied the discourse conveyed about psychiatric pathologies on Twitter considering mostly tweets in English and a limited number of psychiatric disorders terms. This paper proposes the first study to analyze the use of a wide range of psychiatric terms in tweets in French. OBJECTIVE Our aim is to study how generic, nosographic and therapeutic psychiatric terms are used on Twitter in French. More specifically, our study has three complementary goals: (1) to analyze the types of psychiatric word use namely medical, misuse, irrelevant, (2) to analyze the polarity conveyed in the tweets that use these terms (positive/negative/neural), and (3) to compare the frequency of these terms to those observed in related work (mainly in English ). METHODS Our study has been conducted on a corpus of tweets in French posted between 01/01/2016 to 12/31/2018 and collected using dedicated keywords. The corpus has been manually annotated by clinical psychiatrists following a multilayer annotation scheme that includes the type of word use and the opinion orientation of the tweet. Two analysis have been performed. First a qualitative analysis to measure the reliability of the produced manual annotation, then a quantitative analysis considering mainly term frequency in each layer and exploring the interactions between them. RESULTS One of the first result is a resource as an annotated dataset . The initial dataset is composed of 22,579 tweets in French containing at least one of the selected psychiatric terms. From this set, experts in psychiatry randomly annotated 3,040 tweets that corresponds to the resource resulting from our work. The second result is the analysis of the annotations; it shows that terms are misused in 45.3% of the tweets and that their associated polarity is negative in 86.2% of the cases. When considering the three types of term use, 59.5% of the tweets are associated to a negative polarity. Misused terms related to psychotic disorders (55.5%) are more frequent to those related to mood disorders (26.5%). CONCLUSIONS Some psychiatric terms are misused in the corpora we studied; which is consistent with the results reported in related work in other languages. Thanks to the great diversity of studied terms, this work highlighted a disparity in the representations and ways of using psychiatric terms. Moreover, our study is important to help psychiatrists to be aware of the term use in new communication media such as social networks which are widely used. This study has the huge advantage to be reproducible thanks to the framework and guidelines we produced; so that the study could be renewed in order to analyze the evolution of term usage. While the newly build dataset is a valuable resource for other analytical studies, it could also serve to train machine learning algorithms to automatically identify stigma in social media.

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