Election Prediction on Twitter: A Systematic Mapping Study

Context. Social media platforms such as Facebook and Twitter carry a big load of people’s opinions about politics and leaders, which makes them a good source of information for researchers to exploit different tasks that include election predictions. Objective. Identify, categorize, and present a comprehensive overview of the approaches, techniques, and tools used in election predictions on Twitter. Method. Conducted a systematic mapping study (SMS) on election predictions on Twitter and provided empirical evidence for the work published between January 2010 and January 2021. Results. This research identified 787 studies related to election predictions on Twitter. 98 primary studies were selected after defining and implementing several inclusion/exclusion criteria. The results show that most of the studies implemented sentiment analysis (SA) followed by volume-based and social network analysis (SNA) approaches. The majority of the studies employed supervised learning techniques, subsequently, lexicon-based approach SA, volume-based, and unsupervised learning. Besides this, 18 types of dictionaries were identified. Elections of 28 countries were analyzed, mainly USA (28%) and Indian (25%) elections. Furthermore, the results revealed that 50% of the primary studies used English tweets. The demographic data showed that academic organizations and conference venues are the most active. Conclusion. The evolution of the work published in the past 11 years shows that most of the studies employed SA. The implementation of SNA techniques is lower as compared to SA. Appropriate political labelled datasets are not available, especially in languages other than English. Deep learning needs to be employed in this domain to get better predictions.

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Data Mining and Machine Learning Techniques for Bank Customers Segmentation: A Systematic Mapping Study

Advances in Intelligent Systems and Computing - Intelligent Systems and Applications ◽

10.1007/978-3-030-55187-2_48 ◽

2020 ◽

pp. 666-684

Author(s):

Maricel Monge ◽

Christian Quesada-López ◽

Alexandra Martínez ◽

Marcelo Jenkins

Keyword(s):

Machine Learning ◽

Data Mining ◽

Machine Learning Techniques ◽

Systematic Mapping Study ◽

Mapping Study ◽

Systematic Mapping ◽

Learning Techniques ◽

Customers Segmentation

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Gamified Mobile Applications for Improving Driving Behavior: A Systematic Mapping Study

Mobile Information Systems ◽

10.1155/2021/6677075 ◽

2021 ◽

Vol 2021 ◽

pp. 1-24

Author(s):

Abderrahim El hafidy ◽

Taoufik Rachad ◽

Ali Idri ◽

Ahmed Zellou

Keyword(s):

Machine Learning ◽

Mobile Applications ◽

Mobile Apps ◽

Driving Behavior ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Systematic Mapping Study ◽

Mapping Study ◽

Systematic Mapping ◽

Learning Techniques

Many research works and official reports approve that irresponsible driving behavior on the road is the main cause of accidents. Consequently, responsible driving behavior can significantly reduce accidents’ number and severity. Therefore, in the research area as well as in the industrial area, mobile technologies are widely exploited in assisting drivers in reducing accident rates and preventing accidents. For instance, several mobile apps are provided to assist drivers in improving their driving behavior. Recently and thanks to mobile cloud computing, smartphones can benefit from the computing power of servers in the cloud for executing machine learning algorithms. Therefore, many mobile applications of driving assistance and control are based on machine learning techniques to adjust their functioning automatically to driver history, context, and profile. Additionally, gamification is a key element in the design of these mobile applications that allow drivers to develop their engagement and motivation to improve their driving behavior. To have an overview concerning existing mobile apps that improve driving behavior, we have chosen to conduct a systematic mapping study about driving behavior mobile apps that exist in the most common mobile apps repositories or that were published as research works in digital libraries. In particular, we should explore their functionalities, the kinds of collected data, the used gamification elements, and the used machine learning techniques and algorithms. We have successfully identified 220 mobile apps that help to improve driving behavior. In this work, we will extract all the data that seem to be useful for the classification and analysis of the functionalities offered by these applications.

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Software Enhancement Effort Prediction Using Machine-Learning Techniques: A Systematic Mapping Study

SN Computer Science ◽

10.1007/s42979-021-00872-6 ◽

2021 ◽

Vol 2 (6) ◽

Author(s):

Zaineb Sakhrawi ◽

Asma Sellami ◽

Nadia Bouassida

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Systematic Mapping Study ◽

Mapping Study ◽

Systematic Mapping ◽

Learning Techniques ◽

Effort Prediction

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A systematic mapping study on gamification applied to e-marketing

Journal of Research in Interactive Marketing ◽

10.1108/jrim-08-2018-0103 ◽

2019 ◽

Vol 13 (3) ◽

pp. 392-410 ◽

Cited By ~ 3

Author(s):

Fakhroddin Noorbehbahani ◽

Fereshteh Salehi ◽

Reza Jafar Zadeh

Keyword(s):

Research Process ◽

Customer Engagement ◽

Systematic Mapping Study ◽

Mapping Study ◽

Systematic Mapping ◽

Comprehensive Overview ◽

Content Type ◽

Study Methodology ◽

Electronic Technologies

Purpose Today, marketing has evolved due to the emergence of new electronic technologies and has shifted to e-marketing. Meanwhile, the gamification and gamified systems is an up-to-date research topic that has attracted the attention of many researchers in recent years. As one of the main goals of marketing is to increase customer engagement and loyalty by persuading and motivating them to participate, the gamification has a great potential for e-marketing. Although much research has been done on the gamification subject in e-marketing, there has not yet been a comprehensive review of these studies. This paper aims to provide a comprehensive overview of the scientific and practical research on gamification applied to e-marketing using the systematic mapping study methodology. Design/methodology/approach Because considerable research has been devoted to gamification since 2011, and the number of papers published in this area has grown steadily from 2011, the paper reviews the publications over the period 2011-2018. The research method includes developing research questions, designing the research process and filtering the findings based on the specified criteria. Findings The findings of this study show the main applications of gamification in e-marketing, the technologies used and the proven benefits of applying this technique in e-marketing. It also provides a classification of the studies in this area. Practical implications This paper helps other researchers to understand the main areas of research in gamification within the marketing discipline and enables them to find the fields needed for future studies. Originality/value The proposed classification can give a comprehensive overview of the scientific and practical actions taken on gamification applied to e-marketing for academics and practitioners. It also enables the readers to find main areas of research and motivates them to apply gamification in e-marketing.

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