Predicting Depression Symptoms in an Arabic Psychological Forum
Recently, social media platforms have been widely used as a communication tool on social networks. Many users have utilized these platforms to reflect on their personal lives. These users differ in terms of background, language, age, and educational level. The close relationship between these platforms and their users has created rich information that is related to these users and can be exploited by researchers. Therefore, in this project, we investigate the application of natural language processing and machine learning on Arabic text for the prediction of depression and evaluate and compare the performance. Our research method is based on the collection of Arabic text from online forums and the application of either a lexicon-based approach or a machine-learning-based approach. And machine learning algorithms are ultimately applied to predict depression symptoms.
The prediction and monitoring of mental disorders via natural language analysis and on the search for various features and clues of the languages for the identification of the sentiments of posts. One of the main mental disorders is depression, and various related data have been collected in the literature for the prediction of depression via various approaches, such as using self-assessment questionnaires, self-declaration of diagnosis, membership to specialized online forums, and manually automatically annotated posts. The annotation for depression-related studies emphasizes the recognition of posts of users who share their depression experiences.
- Less accuracy score
- A small amount of data for training
- It’s suitable for small level
First, the collected dataset is described in detail, and then created corpus is presented, which is followed by a description of the data pre-processing stage. we will introduce our method for predicting depression from Arabic posts using the lexicon-based approach or the machine learning-based approach and we will evaluate the performances of the applied approaches. The Arab Dep Lexicon is a collection of depression-related terms that are more likely to be found in online posts that are written by individuals who are struggling with depression It exploits the strength of the n-gram models that were used to create the Arab Dep lexicon and identifies the best combination of grams for the prediction of depression symptoms.
- Increasing the accuracy score
- Train the model with a large number of features
Software and Hardware Requirements:
- Python idle
- Anaconda navigator
- Jupiter notebook
Ram 2GB or above
Storage 150GB or above