283 research outputs found
Predicting Depression Using Social Media Posts
The use of Social Network Sites (SNS) is on the rise these days, particularly among the younger generations. Users can communicate their interests, feelings, and everyday routines thanks to the availability of social media sites. Many studies show that properly utilizing user-generated content (UGC) can aid in determining people's mental health status. The use of the UGC could aid in the prediction of mental healthparticularly depression where it is a significant medical condition that impairs one's ability to work, learn, eat, sleep, and enjoy life. However, all of the information about a person's mood and negativism can be gather from their SNS user profile. Therefore, this study utilize SNS as a data source by using machine learning models to screen and identify users in categorizing users based on their mental health. The performance of three machine learning models are evaluated to classify the UGC which are : Decision Forest, Neural Network and Support Vector Machine (SVM). The resuls shows that the accuracy and recall result of the Neural Network model is the same as the Support Vector Machine (SVM) model which is 78.27% and 0.042 but Neural Network performs better in the average precision value. This proves that the Neural Network model is the best models for making predictions to determine the level of depression by using social media posts
Sensing Subjective Well-being from Social Media
Subjective Well-being(SWB), which refers to how people experience the quality
of their lives, is of great use to public policy-makers as well as economic,
sociological research, etc. Traditionally, the measurement of SWB relies on
time-consuming and costly self-report questionnaires. Nowadays, people are
motivated to share their experiences and feelings on social media, so we
propose to sense SWB from the vast user generated data on social media. By
utilizing 1785 users' social media data with SWB labels, we train machine
learning models that are able to "sense" individual SWB from users' social
media. Our model, which attains the state-by-art prediction accuracy, can then
be used to identify SWB of large population of social media users in time with
very low cost.Comment: 12 pages, 1 figures, 2 tables, 10th International Conference, AMT
2014, Warsaw, Poland, August 11-14, 2014. Proceeding
The Royal Birth of 2013: Analysing and Visualising Public Sentiment in the UK Using Twitter
Analysis of information retrieved from microblogging services such as Twitter
can provide valuable insight into public sentiment in a geographic region. This
insight can be enriched by visualising information in its geographic context.
Two underlying approaches for sentiment analysis are dictionary-based and
machine learning. The former is popular for public sentiment analysis, and the
latter has found limited use for aggregating public sentiment from Twitter
data. The research presented in this paper aims to extend the machine learning
approach for aggregating public sentiment. To this end, a framework for
analysing and visualising public sentiment from a Twitter corpus is developed.
A dictionary-based approach and a machine learning approach are implemented
within the framework and compared using one UK case study, namely the royal
birth of 2013. The case study validates the feasibility of the framework for
analysis and rapid visualisation. One observation is that there is good
correlation between the results produced by the popular dictionary-based
approach and the machine learning approach when large volumes of tweets are
analysed. However, for rapid analysis to be possible faster methods need to be
developed using big data techniques and parallel methods.Comment: http://www.blessonv.com/research/publicsentiment/ 9 pages. Submitted
to IEEE BigData 2013: Workshop on Big Humanities, October 201
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