There are many reasons to choose Twitter as the source of data for the computational experiments of this work. Focusing on depression as a major health problem recently and main cause for many other regarded as unwelcome issues in public health 1, 2, 3 especially for women that face more stress than men mostly in her daily activates the authors in this study were motivated by studying and building an aided system for the detection and analysis of women’s depression. These influence researchers in public health, to give their attention and efforts to predict some emotional diseases from this huge available interactive rich data.
Inflectional ending es free#
In addition, these online communities provides a range of different helping hands like (1) real experts in the domain searching for publicity and being famous through introducing free contents and/or (2) individuals with experiences in similar situations, evaluating some treatment options, and sources. Accordingly, through social media, we can express our emotions and talk about our events, diseases, institutions, etc. We consume a large portion of time per day to view, share, comment, and like a massive collection of images or text on social media. Social media has become the main to share information (text, audio, image, and video) on social networking sites, to communicate with our family, friends, and colleagues. Mining social media with main objectives of valuable discoveries is an emerging technology today. The model proved its performance on the three datasets and the obtained and reported in this paper results shows its effectiveness. A third benchmark dataset CLPsych 2015 is used for comparative analysis purposes. The presented model is validated using dual datasets extracted from Twitter: the first dataset is general data formed by 700 women from different countries the second contains only 80 women from KSA.
It is beneficially guided by social media content of individual posts and tweets and an essential support from psycho-linguistic for providing the indicator depression signs vocabulary that creates the embedding words necessary for building the applied approach.
In this study, a deep framework of learning accurate detection of women’s depression is proposed. These enormous contents with the right exploit and research leads to valuable discoveries. Currently, a noteworthy volume of information is available and shared every day through participation and communication of individuals on social media.
0 kommentar(er)
