With its increasing use, social media has become a ubiquitous aspect of modern society. Social media users belong to a broad spectrum of age, ranging from teenage kids to old adults. The primary aim of this thesis is to compare and analyze two different approaches, Glove and Word2vec, utilized for the modeling of language within social media posts to assess the mental state of users. These two approaches along with supervised learning indicate whether or not a social media user demonstrates textual usage risk-factors of depression. This thesis analyzes two popular machine learning approaches for classifying users’ linguistic tendencies based on these language modeling approaches and tests the effectiveness of diagnosing individuals for depression, based on social network textual content. This thesis highlights the differences between the two modeling approaches and explores the causality that results in similar conclusions between both methodologies. Utilizing two different algorithms, Random Forest and Support Vector Machines, social media users were classified for exhibiting linguistic risk-factors of depression. This thesis compares the results between both modeling and machine learning algorithms.