scholarly journals Word Analysis of Indonesian Keirsey Temperament

2020 ◽  
Vol 14 (4) ◽  
pp. 365
Author(s):  
Ahmad Fikri Iskandar ◽  
Ema Utami ◽  
Agung Budi Prasetio
Keyword(s):  
Formal Education ◽  
Potential Fields ◽  
Random Forest Algorithm ◽  
Chi Square ◽  
Social Media Data ◽  
Word Analysis ◽  
Keirsey Temperament Sorter ◽  
Surrounding Environment ◽  
Machine Learning Approach ◽  
Media Data

Personality uniquely relates to our feeling and pattern to the aspect of actions. This behavior will change through the experience, formal education, and the surrounding environment. This works based on the Keirsey Temperament Sorter, a personality questionnaire developed by David Keirsey. This model divides the personality into four categories as Idealists, Rationals, Guardians, and Artisans. This concept is commonly recognized for the interpretation of specialist trends, potentially contributes to the process of recruitment or selection, and potential fields for analysis of social media data. Words selected by using Chi-Square with an error of 5%. Accuracy of the lexicon approach is 34%, while the best machine learning approach is Random Forest algorithm with 69.59%

scholarly journals A machine learning approach predicts future risk to suicidal ideation from social media data

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Arunima Roy ◽  
Katerina Nikolitch ◽  
Rachel McGinn ◽  
Safiya Jinah ◽  
William Klement ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Suicidal Ideation ◽  
Learning Approach ◽  
Social Media Data ◽  
Machine Learning Approach ◽  
Future Risk ◽  
Media Data

scholarly journals An Analysis of Spatial Accessibility Changes According to the Attractiveness Index of Public Libraries Using Social Media Data

2021 ◽  
Vol 13 (16) ◽  
pp. 9087
Author(s):  
Jiyoung Kim ◽  
Jiwon Lee
Keyword(s):  
Social Media ◽  
Formal Education ◽  
Public Libraries ◽  
Spatial Accessibility ◽  
Supply Side ◽  
Social Media Data ◽  
The Republic ◽  
Questionnaire Surveys ◽  
Use Factors ◽  
Media Data

Public libraries provide equitable access to information for all citizens, and they play an important role in preserving and promoting culture, formal education and self-education, and enriching leisure time. Accordingly, there has been an increasing amount of research on the use factors and accessibility of public libraries, but research on the accessibility of public libraries in non-Western cities is insufficient compared to the corresponding research on other public facilities. In particular, in high-density cities such as Seoul, the Republic of Korea, it may be desirable in terms of sustainability to focus on the qualitative, rather than the quantitative, expansion of public libraries. In previous studies, the attractive factors on the supply side were analyzed using questionnaire surveys, but in this study, the attractive factors for users were quantified in the form of the library attraction index by means of user-generated contents such as location-based social media, and the accessibility was analyzed based on this. The results showed that many public libraries have high accessibility, with a high library attraction index. Therefore, these findings indicate that the qualitative expansion of public libraries is important for information equality. It is meaningful that this study analyzed the attractive factors on the supply side by analyzing the contents generated by users.

scholarly journals USING GEO GEO-TAGGED FLICKR IMAGES TO EXPLORE THE CORRELATION BETWEEN LAND COVER CLASSES AND THE LOCATION OF BIRD OBSERVATIONS

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 189-194
Author(s):  
M. Lotfian ◽  
J. Ingensand
Keyword(s):  
Land Cover ◽  
Citizen Science ◽  
Bird Species ◽  
Science Data ◽  
Chi Square ◽  
Social Media Data ◽  
Photo Sharing ◽  
Chi Square Test ◽  
Media Data ◽  
Flickr Images

Abstract. Social media data are becoming potential sources of passive VGI (Volunteered Geographic Information) and citizen science, in particular with regard to location-based environmental monitoring. Flickr, as one of the largest photo-sharing platforms, has been used in various environmental analyses from natural disaster prediction to wildlife monitoring. In this article, we have used bird photos from Flickr to illustrate the spatial distribution of bird species in Switzerland, and most importantly to see the correlation between the location of bird species and land cover types. A chi-square test of independence has been applied to illustrate the association between bird species and land cover classes and results illustrated a statistically significant association between the two variables. Furthermore, species distributions in Flickr were compared to eBird data, and the results demonstrated that Flickr can be a possible complementary source to citizen science data.

scholarly journals Pemantauan Perhatian Publik terhadap Pandemi COVID-19 melalui Klasifikasi Teks dengan Deep Learning

2021 ◽  
Vol 5 (1) ◽  
pp. 193-202
Author(s):  
Novrindah Alvi Hasanah ◽  
Nanik Suciati ◽  
Diana Purwitasari
Keyword(s):  
Deep Learning ◽  
Public Concern ◽  
Public Attention ◽  
Social Media Data ◽  
Twitter Data ◽  
Surrounding Environment ◽  
Public Behavior ◽  
The Media ◽  
The Right ◽  
Media Data

Monitoring public concern in the surrounding environment to certain events is done to address changes in public behavior individually and socially. The results of monitoring public attention can be used as a benchmark for related parties in making the right policies and strategies to deal with changes in public behavior as a result of the COVID-19 pandemic. Monitoring public attention can be done using Twitter social media data because the users of the media are quite high, so that they can represent the aspirations of the general public. However, Twitter data contains varied topics, so a classification process is required to obtain data related to COVID-19. Classification is done by using word embedding variations (Word2Vec and fastText) and deep learning variations (CNN, RNN, and LSTM) to get the classification results with the best accuracy. The percentage of COVID-19 data based on the best accuracy is calculated to determine how high the public's attention is to the COVID-19 pandemic. Experiments were carried out with three scenarios, which were differentiated by the number of data trains. The classification results with the best accuracy are obtained by the combination of fasText and LSTM which shows the highest accuracy of 97.86% and the lowest of 93.63%. The results of monitoring public attention to the time vulnerability between June and October show that the highest public attention to COVID-19 is in June.

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